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Online survey built via Qualtrics and disseminated via Prolific to gather data relating to the measurement of aggression/aggressive behaviour in the general population

Online survey built via Qualtrics and disseminated via Prolific to gather data relating to the measurement of aggression/aggressive behaviour in the general population

This article is from International Journal of Health Geographics , volume 12 . Abstract Background: There is a rising public and political demand for prospective cancer cluster monitoring. But there is little empirical evidence on the performance of established cluster detection tests under conditions of small and heterogeneous sample sizes and varying spatial scales, such as are the case for most existing population-based cancer registries. Therefore this simulation study aims to evaluate different cluster detection methods, implemented in the open soure environment R, in their ability to identify clusters of lung cancer using real-life data from an epidemiological cancer registry in Germany. Methods: Risk surfaces were constructed with two different spatial cluster types, representing a relative risk of RR = 2.0 or of RR = 4.0, in relation to the overall background incidence of lung cancer, separately for men and women. Lung cancer cases were sampled from this risk surface as geocodes using an inhomogeneous Poisson process. The realisations of the cancer cases were analysed within small spatial (census tracts, N = 1983) and within aggregated large spatial scales (communities, N = 78). Subsequently, they were submitted to the cluster detection methods. The test accuracy for cluster location was determined in terms of detection rates (DR), false-positive (FP) rates and positive predictive values. The Bayesian smoothing models were evaluated using ROC curves. Results: With moderate risk increase (RR = 2.0), local cluster tests showed better DR (for both spatial aggregation scales > 0.90) and lower FP rates (both 

While perception tasks such as visual object recognition and text understanding play an important role in human intelligence, the subsequent tasks that involve inference, reasoning and planning require an even higher level of intelligence. The past few years have seen major advances in many perception tasks using deep learning models. For higher-level inference, however, probabilistic graphical models with their Bayesian nature are still more powerful and flexible. To achieve integrated intelligence that involves both perception and inference, it is naturally desirable to tightly integrate deep learning and Bayesian models within a principled probabilistic framework, which we call Bayesian deep learning. In this unified framework, the perception of text or images using deep learning can boost the performance of higher-level inference and in return, the feedback from the inference process is able to enhance the perception of text or images. This paper proposes a general framework for Bayesian deep learning and reviews its recent applications on recommender systems, topic models, and control. In this paper, we also discuss the relationship and differences between Bayesian deep learning and other related topics like Bayesian treatment of neural networks.

Data clustering, including problems such as finding network communities, can be put into a systematic framework by means of a Bayesian approach. The application of Bayesian approaches to real problems can be, however, quite challenging. In most cases the solution is explored via Monte Carlo sampling or variational methods. Here we work further on the application of variational methods to clustering problems. We introduce generative models based on a hidden group structure and prior distributions. We extend previous attends by Jaynes, and derive the prior distributions based on symmetry arguments. As a case study we address the problems of two-sides clustering real value data and clustering data represented by a hypergraph or bipartite graph. From the variational calculations, and depending on the starting statistical model for the data, we derive a variational Bayes algorithm, a generalized version of the expectation maximization algorithm with a built in penalization for model complexity or bias. We demonstrate the good performance of the variational Bayes algorithm using test examples.

Data clustering, including problems such as finding network communities, can be put into a systematic framework by means of a Bayesian approach. The application of Bayesian approaches to real problems can be, however, quite challenging. In most cases the solution is explored via Monte Carlo sampling or variational methods. Here we work further on the application of variational methods to clustering problems. We introduce generative models based on a hidden group structure and prior distributions. We extend previous attends by Jaynes, and derive the prior distributions based on symmetry arguments. As a case study we address the problems of two-sides clustering real value data and clustering data represented by a hypergraph or bipartite graph. From the variational calculations, and depending on the starting statistical model for the data, we derive a variational Bayes algorithm, a generalized version of the expectation maximization algorithm with a built in penalization for model complexity or bias. We demonstrate the good performance of the variational Bayes algorithm using test examples.

Model selection is important in any statistical analysis, and the primary goal is to find the preferred (or most parsimonious) model, based on certain criteria, from a set of candidate models given data. Several recent publications have employed the deviance information criterion (DIC) to do model selection among different forms of multilevel item response theory models (MLIRT). The majority of the practitioners use WinBUGS for implementing MCMC algorithms for MLIRT models, and the default version of DIC provided by WinBUGS focused on the measurement-level parameters only. The results herein show that this version of DIC is inappropriate. This study introduces five variants of DIC as a model selection index for MLIRT models with dichotomous outcomes. Considering a multilevel IRT model with three levels, five forms of DIC are formed: first-level conditional DIC computed from the measurement model only, which is the index given by many software packages such as WinBUGS; second-level marginalized DIC and second-level joint DIC computed from the second-level model; and top-level marginalized DIC and top-level joint DIC computed from the entire model. We evaluate the performance of the five model selection indices via simulation studies. The manipulated factors include the number of groups, the number of second-level covariates, the number of top-level covariates, and the types of measurement models (one-parameter vs. two-parameter). Considering the computational viability and interpretability, the second-level joint DIC is recommended for MLIRT models under our simulated conditions. [This article was published in "Journal of Educational Measurement" (EJ1208645).]

Model selection is important in any statistical analysis, and the primary goal is to find the preferred (or most parsimonious) model, based on certain criteria, from a set of candidate models given data. Several recent publications have employed the deviance information criterion (DIC) to do model selection among different forms of multilevel item response theory models (MLIRT). The majority of the practitioners use WinBUGS for implementing MCMC algorithms for MLIRT models, and the default version of DIC provided by WinBUGS focused on the measurement-level parameters only. The results herein show that this version of DIC is inappropriate. This study introduces five variants of DIC as a model selection index for MLIRT models with dichotomous outcomes. Considering a multilevel IRT model with three levels, five forms of DIC are formed: first-level conditional DIC computed from the measurement model only, which is the index given by many software packages such as WinBUGS; second-level marginalized DIC and second-level joint DIC computed from the second-level model; and top-level marginalized DIC and top-level joint DIC computed from the entire model. We evaluate the performance of the five model selection indices via simulation studies. The manipulated factors include the number of groups, the number of second-level covariates, the number of top-level covariates, and the types of measurement models (one-parameter vs. two-parameter). Considering the computational viability and interpretability, the second-level joint DIC is recommended for MLIRT models under our simulated conditions. [This article was published in "Journal of Educational Measurement" (EJ1208645).]

Model selection is important in any statistical analysis, and the primary goal is to find the preferred (or most parsimonious) model, based on certain criteria, from a set of candidate models given data. Several recent publications have employed the deviance information criterion (DIC) to do model selection among different forms of multilevel item response theory models (MLIRT). The majority of the practitioners use WinBUGS for implementing MCMC algorithms for MLIRT models, and the default version of DIC provided by WinBUGS focused on the measurement-level parameters only. The results herein show that this version of DIC is inappropriate. This study introduces five variants of DIC as a model selection index for MLIRT models with dichotomous outcomes. Considering a multilevel IRT model with three levels, five forms of DIC are formed: first-level conditional DIC computed from the measurement model only, which is the index given by many software packages such as WinBUGS; second-level marginalized DIC and second-level joint DIC computed from the second-level model; and top-level marginalized DIC and top-level joint DIC computed from the entire model. We evaluate the performance of the five model selection indices via simulation studies. The manipulated factors include the number of groups, the number of second-level covariates, the number of top-level covariates, and the types of measurement models (one-parameter vs. two-parameter). Considering the computational viability and interpretability, the second-level joint DIC is recommended for MLIRT models under our simulated conditions. [This article was published in "Journal of Educational Measurement" (EJ1208645).]

Discussion of "Impact of Frequentist and Bayesian Methods on Survey Sampling Practice: A Selective Appraisal" by J. N. K. Rao [arXiv:1108.2356]

The goal of this paper is to compare several widely used Bayesian model selection methods in practical model selection problems, highlight their differences and give recommendations about the preferred approaches. We focus on the variable subset selection for regression and classification and perform several numerical experiments using both simulated and real world data. The results show that the optimization of a utility estimate such as the cross-validation (CV) score is liable to finding overfitted models due to relatively high variance in the utility estimates when the data is scarce. This can also lead to substantial selection induced bias and optimism in the performance evaluation for the selected model. From a predictive viewpoint, best results are obtained by accounting for model uncertainty by forming the full encompassing model, such as the Bayesian model averaging solution over the candidate models. If the encompassing model is too complex, it can be robustly simplified by the projection method, in which the information of the full model is projected onto the submodels. This approach is substantially less prone to overfitting than selection based on CV-score. Overall, the projection method appears to outperform also the maximum a posteriori model and the selection of the most probable variables. The study also demonstrates that the model selection can greatly benefit from using cross-validation outside the searching process both for guiding the model size selection and assessing the predictive performance of the finally selected model.

We develop a Bayesian model for globular clusters composed of multiple stellar populations, extending earlier statistical models for open clusters composed of simple (single) stellar populations (vanDyk et al. 2009, Stein et al. 2013). Specifically, we model globular clusters with two populations that differ in helium abundance. Our model assumes a hierarchical structuring of the parameters in which physical properties---age, metallicity, helium abundance, distance, absorption, and initial mass---are common to (i) the cluster as a whole or to (ii) individual populations within a cluster, or are unique to (iii) individual stars. An adaptive Markov chain Monte Carlo (MCMC) algorithm is devised for model fitting that greatly improves convergence relative to its precursor non-adaptive MCMC algorithm. Our model and computational tools are incorporated into an open-source software suite known as BASE-9. We use numerical studies to demonstrate that our method can recover parameters of two-population clusters, and also show model misspecification can potentially be identified. As a proof of concept, we analyze the two stellar populations of globular cluster NGC 5272 using our model and methods. (BASE-9 is available from GitHub: https://github.com/argiopetech/base/releases).

The $k$-nearest neighbor classification method ($k$-NNC) is one of the simplest nonparametric classification methods. The mutual $k$-NN classification method (M$k$NNC) is a variant of $k$-NNC based on mutual neighborship. We propose another variant of $k$-NNC, the symmetric $k$-NN classification method (S$k$NNC) based on both mutual neighborship and one-sided neighborship. The performance of M$k$NNC and S$k$NNC depends on the parameter $k$ as the one of $k$-NNC does. We propose the ways how M$k$NN and S$k$NN classification can be performed based on Bayesian mutual and symmetric $k$-NN regression methods with the selection schemes for the parameter $k$. Bayesian mutual and symmetric $k$-NN regression methods are based on Gaussian process models, and it turns out that they can do M$k$NN and S$k$NN classification with new encodings of target values (class labels). The simulation results show that the proposed methods are better than or comparable to $k$-NNC, M$k$NNC and S$k$NNC with the parameter $k$ selected by the leave-one-out cross validation method not only for an artificial data set but also for real world data sets.

Investigation of neural circuit functioning often requires statistical interpretation of events in subthreshold electrophysiological recordings. This problem is non-trivial because recordings may have moderate levels of structured noise and events may have distinct kinetics. In addition, novel experimental designs that combine optical and electrophysiological methods will depend upon statistical tools that combine multimodal data. We present a Bayesian approach for inferring the timing, strength, and kinetics of postsynaptic currents (PSCs) from voltage-clamp recordings on a per event basis. The simple generative model for a single voltage-clamp recording flexibly extends to include network-level structure to enable experiments designed to probe synaptic connectivity. We validate the approach on simulated and real data. We also demonstrate that extensions of the basic PSC detection algorithm can handle recordings contaminated with optically evoked currents, and we simulate a scenario in which calcium imaging observations, available for a subset of neurons, can be fused with electrophysiological data to achieve higher temporal resolution. We apply this approach to simulated and real ground truth data to demonstrate its higher sensitivity in detecting small signal-to-noise events and its increased robustness to noise compared to standard methods for detecting PSCs. The new Bayesian event analysis approach for electrophysiological recordings should allow for better estimation of physiological parameters under more variable conditions and help support new experimental designs for circuit mapping.

Investigation of neural circuit functioning often requires statistical interpretation of events in subthreshold electrophysiological recordings. This problem is non-trivial because recordings may have moderate levels of structured noise and events may have distinct kinetics. In addition, novel experimental designs that combine optical and electrophysiological methods will depend upon statistical tools that combine multimodal data. We present a Bayesian approach for inferring the timing, strength, and kinetics of postsynaptic currents (PSCs) from voltage-clamp recordings on a per event basis. The simple generative model for a single voltage-clamp recording flexibly extends to include network-level structure to enable experiments designed to probe synaptic connectivity. We validate the approach on simulated and real data. We also demonstrate that extensions of the basic PSC detection algorithm can handle recordings contaminated with optically evoked currents, and we simulate a scenario in which calcium imaging observations, available for a subset of neurons, can be fused with electrophysiological data to achieve higher temporal resolution. We apply this approach to simulated and real ground truth data to demonstrate its higher sensitivity in detecting small signal-to-noise events and its increased robustness to noise compared to standard methods for detecting PSCs. The new Bayesian event analysis approach for electrophysiological recordings should allow for better estimation of physiological parameters under more variable conditions and help support new experimental designs for circuit mapping.

In this paper, we consider the so-called Shape Invariant Model which stands for the estimation of a function f0 submitted to a random translation of law g0 in a white noise model. We are interested in such a model when the law of the deformations is unknown. We aim to recover the law of the process P(f0,g0). In this perspective, we adopt a Bayesian point of view and find prior on f and g such that the posterior distribution concentrates at a polynomial rate around P(f0,g0) when n goes to infinity. We intensively use some Bayesian non parametric tools coupled with mixture models and believe that some of our results obtained on this mixture framework may be also of interest for frequentist point of view.

Discussion of "Bayesian Models and Methods in Public Policy and Government Settings" by S. E. Fienberg [arXiv:1108.2177]

The feasibility and effectiveness of a Bayesian method for estimating regressions in m groups is studied by application of the method to data from the Basic Research Service of The American College Testing Program. Evidence supports the belief that in many testing applications the collateral information obtained from each subset of m-1 colleges will be useful for the estimation of the regression in the m-th college. Specifically, on cross-validation in a second sample, the Bayesian predictions had a smaller mean squared error in each of 22 colleges, the reduction averaging 9.7%, when compared with the least squares predictions when four predictor variables were used on a quarter sample in the 22 colleges where initial within-college sample sizes ranged from 26 to 184. Furthermore, even when based on the full sample within each college, the least squares predictions had an average cross-validated mean squared error only barely less than the Bayesian predictions based on the quarter sample. The most apparent benefit of the Bayesian method is that it permits regression to be done in subpopulations where sample sizes are small and where the regressions are different in the subpopulations. In the present study, a decrease of more than 10% in mean squared error was obtained using this approach. (Author)

The feasibility and effectiveness of a Bayesian method for estimating regressions in m groups is studied by application of the method to data from the Basic Research Service of The American College Testing Program. Evidence supports the belief that in many testing applications the collateral information obtained from each subset of m-1 colleges will be useful for the estimation of the regression in the m-th college. Specifically, on cross-validation in a second sample, the Bayesian predictions had a smaller mean squared error in each of 22 colleges, the reduction averaging 9.7%, when compared with the least squares predictions when four predictor variables were used on a quarter sample in the 22 colleges where initial within-college sample sizes ranged from 26 to 184. Furthermore, even when based on the full sample within each college, the least squares predictions had an average cross-validated mean squared error only barely less than the Bayesian predictions based on the quarter sample. The most apparent benefit of the Bayesian method is that it permits regression to be done in subpopulations where sample sizes are small and where the regressions are different in the subpopulations. In the present study, a decrease of more than 10% in mean squared error was obtained using this approach. (Author)

Minimum mean squared error (MMSE) estimators of signals from samples corrupted by jitter (timing noise) and additive noise are nonlinear, even when the signal prior and additive noise have normal distributions. This paper develops a stochastic algorithm based on Gibbs sampling and slice sampling to approximate the optimal MMSE estimator in this Bayesian formulation. Simulations demonstrate that this nonlinear algorithm can improve significantly upon the linear MMSE estimator, as well as the EM algorithm approximation to the maximum likelihood (ML) estimator used in classical estimation. Effective off-chip post-processing to mitigate jitter enables greater jitter to be tolerated, potentially reducing on-chip ADC power consumption.

(Jaynes') Method of (Shannon-Kullback's) Relative Entropy Maximization (REM or MaxEnt) can be - at least in the discrete case - according to the Maximum Probability Theorem (MPT) viewed as an asymptotic instance of the Maximum Probability method (MaxProb). A simple bayesian interpretation of MaxProb is given here. MPT carries the interpretation over into REM.

(Jaynes') Method of (Shannon-Kullback's) Relative Entropy Maximization (REM or MaxEnt) can be - at least in the discrete case - according to the Maximum Probability Theorem (MPT) viewed as an asymptotic instance of the Maximum Probability method (MaxProb). A simple bayesian interpretation of MaxProb is given here. MPT carries the interpretation over into REM.

(Jaynes') Method of (Shannon-Kullback's) Relative Entropy Maximization (REM or MaxEnt) can be - at least in the discrete case - according to the Maximum Probability Theorem (MPT) viewed as an asymptotic instance of the Maximum Probability method (MaxProb). A simple bayesian interpretation of MaxProb is given here. MPT carries the interpretation over into REM.

To understand their data better, astronomers need to use statistical tools that are more advanced than traditional ``freshman lab'' statistics. As an illustration, the problem of combining apparently incompatible measurements of a quantity is presented from both the traditional, and a more sophisticated Bayesian, perspective. Explicit formulas are given for both treatments. Results are shown for the value of the Hubble Constant, and a 95% confidence interval of 66 < H0 < 82 (km/s/Mpc) is obtained.

In this paper we briefly review the main methodological aspects concerned with the application of the Bayesian approach to model choice and model averaging in the context of variable selection in regression models. This includes prior elicitation, summaries of the posterior distribution and computational strategies. We then examine and compare various publicly available {\tt R}-packages for its practical implementation summarizing and explaining the differences between packages and giving recommendations for applied users. We find that all packages reviewed lead to very similar results, but there are potentially important differences in flexibility and efficiency of the packages.

Explains how Bayesian networks can tackle the limitations of pure data-driven statistical machine learning methods when applied to observational data. This is the lecture I was due to present at the NHS Health and Care Analytics Conference, 5 July 2023. For the back story on this see: https://wherearethenumbers.substack.com/p/an-update-on-my-nhs-conference-cancellation

This article is from BMC Bioinformatics , volume 12 . Abstract Background: A Bayesian approach based on a Dirichlet process (DP) prior is useful for inferring genetic population structures because it can infer the number of populations and the assignment of individuals simultaneously. However, the properties of the DP prior method are not well understood, and therefore, the use of this method is relatively uncommon. We characterized the DP prior method to increase its practical use. Results: First, we evaluated the usefulness of the sequentially-allocated merge-split (SAMS) sampler, which is a technique for improving the mixing of Markov chain Monte Carlo algorithms. Although this sampler has been implemented in a preceding program, HWLER, its effectiveness has not been investigated. We showed that this sampler was effective for population structure analysis. Implementation of this sampler was useful with regard to the accuracy of inference and computational time. Second, we examined the effect of a hyperparameter for the prior distribution of allele frequencies and showed that the specification of this parameter was important and could be resolved by considering the parameter as a variable. Third, we compared the DP prior method with other Bayesian clustering methods and showed that the DP prior method was suitable for data sets with unbalanced sample sizes among populations. In contrast, although current popular algorithms for population structure analysis, such as those implemented in STRUCTURE, were suitable for data sets with uniform sample sizes, inferences with these algorithms for unbalanced sample sizes tended to be less accurate than those with the DP prior method. Conclusions: The clustering method based on the DP prior was found to be useful because it can infer the number of populations and simultaneously assign individuals into populations, and it is suitable for data sets with unbalanced sample sizes among populations. Here we presented a novel program, DPART, that implements the SAMS sampler and can consider the hyperparameter for the prior distribution of allele frequencies to be a variable.

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This is a network meta-analysis to investigate the efficacy and safety of acupuncture methods for nonspecific low back pain. We will compare acupuncture methods for nonspecific low back pain by bayesian network meta‑analysis and rank the priority of acupuncture methods to assess the efficacy and safety of diverse acupuncture methods for nonspecific low back pain treatment.

Cognitive radio is an exciting wireless technology that has been introduced for the efficient used of spectrum. Using cognitive radios (CRs), the secondary users (unlicensed users) are allowed to use the spectrum which is originally allocated to primary users (PUs) as far as the active primary users are not using it temporarily. In order to prevent harmful interference to primary users, the SUs need to perform spectrum sensing before transmitting signal over the spectrum. In this paper we use an optimal Bayesian detector for digitally modulated primary user to improve the spectrum utilization, without prior knowledge of transmitted sequence of the primary signals. And further suboptimal detectors in low and high SNR regime. We provide the performance analysis in terms of Detection probability and False alarm probability. Abdul Hameed Ansari | Narode Sweety S."Performance Analysis of Bayesian Methods to for the Spectrum Utilization in Cognitive Radio" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-1 | Issue-5 , August 2017, URL: http://www.ijtsrd.com/papers/ijtsrd2385.pdf Article URL: http://www.ijtsrd.com/engineering/electronics-and-communication-engineering/2385/performance-analysis-of-bayesian-methods-to-for-the-spectrum-utilization-in-cognitive-radio/abdul-hameed-ansari

We present a parameter estimation procedure based on a Bayesian framework by applying a Markov Chain Monte Carlo algorithm to the calibration of the dynamical parameters of a space based gravitational wave detector. The method is based on the Metropolis-Hastings algorithm and a two-stage annealing treatment in order to ensure an effective exploration of the parameter space at the beginning of the chain. We compare two versions of the algorithm with an application to a LISA Pathfinder data analysis problem. The two algorithms share the same heating strategy but with one moving in coordinate directions using proposals from a multivariate Gaussian distribution, while the other uses the natural logarithm of some parameters and proposes jumps in the eigen-space of the Fisher Information matrix. The algorithm proposing jumps in the eigen-space of the Fisher Information matrix demonstrates a higher acceptance rate and a slightly better convergence towards the equilibrium parameter distributions in the application to LISA Pathfinder data . For this experiment, we return parameter values that are all within $\sim1\sigma$ of the injected values. When we analyse the accuracy of our parameter estimation in terms of the effect they have on the force-per-unit test mass noise estimate, we find that the induced errors are three orders of magnitude less than the expected experimental uncertainty in the power spectral density.

Bayesian network meta-analysis will be conducted using STATA V.14.0 and WinBUGS V.1.4.3 to compare the efficacy of different acupuncture methods for diabetic peripheral neuropathy (DPN).

Bayesian network meta-analysis will be conducted using STATA V.14.0 and WinBUGS V.1.4.3 to compare the efficacy of different acupuncture methods for diabetic peripheral neuropathy (DPN).

Bayesian network meta-analysis will be conducted using STATA V.14.0 and WinBUGS V.1.4.3 to compare the efficacy of different acupuncture methods for diabetic peripheral neuropathy (DPN).

This article is from BMC Medical Research Methodology , volume 12 . Abstract Background: Computerized adaptive testing (CAT) is being applied to health outcome measures developed as paper-and-pencil (P&P) instruments. Differences in how respondents answer items administered by CAT vs. P&P can increase error in CAT-estimated measures if not identified and corrected. Method: Two methods for detecting item-level mode effects are proposed using Bayesian estimation of posterior distributions of item parameters: (1) a modified robust Z (RZ) test, and (2) 95% credible intervals (CrI) for the CAT-P&P difference in item difficulty. A simulation study was conducted under the following conditions: (1) data-generating model (one- vs. two-parameter IRT model); (2) moderate vs. large DIF sizes; (3) percentage of DIF items (10% vs. 30%), and (4) mean difference in θ estimates across modes of 0 vs. 1 logits. This resulted in a total of 16 conditions with 10 generated datasets per condition. Results: Both methods evidenced good to excellent false positive control, with RZ providing better control of false positives and with slightly higher power for CrI, irrespective of measurement model. False positives increased when items were very easy to endorse and when there with mode differences in mean trait level. True positives were predicted by CAT item usage, absolute item difficulty and item discrimination. RZ outperformed CrI, due to better control of false positive DIF. Conclusions: Whereas false positives were well controlled, particularly for RZ, power to detect DIF was suboptimal. Research is needed to examine the robustness of these methods under varying prior assumptions concerning the distribution of item and person parameters and when data fail to conform to prior assumptions. False identification of DIF when items were very easy to endorse is a problem warranting additional investigation.

The long term goal of this project is to develop efficient inversion algorithms for successful geoacoustic parameter estimation, inversion for sound-speed in the water-column, and source localization, exploiting (fully or partially) the physics of the propagation medium. Algorithms are designed for inversion via the extraction of features of the acoustic field and optimization. The potential of analytic approaches is also investigated.

This article is from PLoS ONE , volume 9 . Abstract The aim of this study was to evaluate the impact of genotype imputation on the performance of the GBLUP and Bayesian methods for genomic prediction. A total of 10,309 Holstein bulls were genotyped on the BovineSNP50 BeadChip (50 k). Five low density single nucleotide polymorphism (SNP) panels, containing 6,177, 2,480, 1,536, 768 and 384 SNPs, were simulated from the 50 k panel. A fraction of 0%, 33% and 66% of the animals were randomly selected from the training sets to have low density genotypes which were then imputed into 50 k genotypes. A GBLUP and a Bayesian method were used to predict direct genomic values (DGV) for validation animals using imputed or their actual 50 k genotypes. Traits studied included milk yield, fat percentage, protein percentage and somatic cell score (SCS). Results showed that performance of both GBLUP and Bayesian methods was influenced by imputation errors. For traits affected by a few large QTL, the Bayesian method resulted in greater reductions of accuracy due to imputation errors than GBLUP. Including SNPs with largest effects in the low density panel substantially improved the accuracy of genomic prediction for the Bayesian method. Including genotypes imputed from the 6 k panel achieved almost the same accuracy of genomic prediction as that of using the 50 k panel even when 66% of the training population was genotyped on the 6 k panel. These results justified the application of the 6 k panel for genomic prediction. Imputations from lower density panels were more prone to errors and resulted in lower accuracy of genomic prediction. But for animals that have close relationship to the reference set, genotype imputation may still achieve a relatively high accuracy.

Parameter ensembles or sets of random effects constitute one of the cornerstones of modern statistical practice. This is especially the case in Bayesian hierarchical models, where several decision theoretic frameworks can be deployed. The estimation of these parameter ensembles may substantially vary depending on which inferential goals are prioritised by the modeller. Since one may wish to satisfy a range of desiderata, it is therefore of interest to investigate whether some sets of point estimates can simultaneously meet several inferential objectives. In this thesis, we will be especially concerned with identifying ensembles of point estimates that produce good approximations of (i) the true empirical quantiles and empirical quartile ratio (QR) and (ii) provide an accurate classification of the ensemble's elements above and below a given threshold. For this purpose, we review various decision-theoretic frameworks, which have been proposed in the literature in relation to the optimisation of different aspects of the empirical distribution of a parameter ensemble. This includes the constrained Bayes (CB), weighted-rank squared error loss (WRSEL), and triple-goal (GR) ensembles of point estimates. In addition, we also consider the set of maximum likelihood estimates (MLEs) and the ensemble of posterior means --the latter being optimal under the summed squared error loss (SSEL). Firstly, we test the performance of these different sets of point estimates as plug-in estimators for the empirical quantiles and empirical QR under a range of synthetic scenarios encompassing both spatial and non-spatial simulated data sets. Performance evaluation is here conducted using the posterior regret. Secondly, two threshold classification losses (TCLs) --weighted and unweighted-- are formulated and formally optimised. The performance of these decision-theoretic tools is also evaluated on real data sets.

The Bayesian approach to Inverse Problems relies predominantly on Markov Chain Monte Carlo methods for posterior inference. The typical nonlinear concentration of posterior measure observed in many such Inverse Problems presents severe challenges to existing simulation based inference methods. Motivated by these challenges the exploitation of local geometric information in the form of covariant gradients, metric tensors, Levi-Civita connections, and local geodesic flows, have been introduced to more effectively locally explore the configuration space of the posterior measure. However, obtaining such geometric quantities usually requires extensive computational effort and despite their effectiveness affect the applicability of these geometrically-based Monte Carlo methods. In this paper we explore one way to address this issue by the construction of an emulator of the model from which all geometric objects can be obtained in a much more computationally feasible manner. The main concept is to approximate the geometric quantities using a Gaussian Process emulator which is conditioned on a carefully chosen design set of configuration points, which also determines the quality of the emulator. To this end we propose the use of statistical experiment design methods to refine a potentially arbitrarily initialized design online without destroying the convergence of the resulting Markov chain to the desired invariant measure. The practical examples considered in this paper provide a demonstration of the significant improvement possible in terms of computational loading suggesting this is a promising avenue of further development.

In some cases, computational benefit can be gained by exploring the hyper parameter space using a deterministic set of grid points instead of a Markov chain. We view this as a numerical integration problem and make three unique contributions. First, we explore the space using low discrepancy point sets instead of a grid. This allows for accurate estimation of marginals of any shape at a much lower computational cost than a grid based approach and thus makes it possible to extend the computational benefit to a hyper parameter space with higher dimensionality (10 or more). Second, we propose a new, quick and easy method to estimate the marginal using a least squares polynomial and prove the conditions under which this polynomial will converge to the true marginal. Our results are valid for a wide range of point sets including grids, random points and low discrepancy points. Third, we show that further accuracy and efficiency can be gained by taking into consideration the functional decomposition of the integrand and illustrate how this can be done using anchored f-ANOVA on weighted spaces.

This article addresses the problem of efficient Bayesian inference in dynamic systems using particle methods and makes a number of contributions. First, we develop a correlated pseudo-marginal (CPM) approach for Bayesian inference in state space (SS) models that is based on filtering the disturbances, rather than the states. This approach is useful when the state transition density is intractable or inefficient to compute, and also when the dimension of the disturbance is lower than the dimension of the state. Second, we propose a block pseudo-marginal (BPM) method that uses as the estimate of the likelihood the average of G independent unbiased estimates of the likelihood. We associate a set of underlying uniform of standard normal random numbers used to construct each of the individual unbiased likelihood estimates and then use component-wise Markov Chain Monte Carlo to update the parameter vector jointly with one set of these random numbers at a time. This induces a correlation of approximately 1-1/G between the logs of the estimated likelihood at the proposed and current values of the model parameters. Third, we show for some non-stationary state space models that the BPM approach is much more efficient than the CPM approach, because it is difficult to translate the high correlation in the underlying random numbers to high correlation between the logs of the likelihood estimates. Although our focus has been on applying the BPM method to state space models, our results and approach can be used in a wide range of applications of the PM method, such as panel data models, subsampling problems and approximate Bayesian computation.

The impetus for this study was provided by the interest of the U.S. Army Operational Test and Evaluation Agency (OTEA) to investigate the possible application of Bayesian statistical analysis and decision theory to sample size determination for operational testing. In order to understand some of the procedures discussed later in this study, a basic knowledge of the nature of operational testing as performed by OTEA is necessary. The purpose of operational testing is to provide a source of data from which estimates may be developed as to the military utility, operational effectiveness and operational suitability of new weapon systems. This data is obtained through a sequence of three operational tests; each test in the sequence is completed and the results analyzed prior to beginning the next test. For ease of reference, these tests will be referred to as Operational Test I (OT I), Operational Test II (OT II) and Operational Test III (OT III). Once the data has been collected and the estimates developed an assessment is made of the new system's desirability as compared to systems which are already available.

This article is from BMJ Open , volume 3 . Abstract Objectives: To identify published closed-loop Bayesian mixed treatment comparisons (MTCs) and to summarise characteristics regarding their conduct and reporting. Design: Systematic review. Methods: We searched multiple bibliographic databases (January 2006–31 July 2011) for full-text, English language publications of Bayesian MTCs comparing the effectiveness or safety of ≥3 interventions based on randomised controlled trials and having at least one closed loop. Methodological and reporting characteristics of MTCs were extracted in duplicate and summarised descriptively. Results: We identified 34 Bayesian MTCs spanning 13 clinical areas. Publication of MTCs increased over the 5-year period; with 76.5% published during or after 2009. MTCs included a mean (±SD) of 35.9±30.1 trials (n=33 459±71 233 participants) and 8.5±4.3 interventions (85.7% pharmacological). Non-informative and informative prior distributions were reported to be used in 44.1% and 8.8% of MTCs, respectively, with the remainder failing to specify the prior used. A random-effects model was used to analyse the networks of trials in 58.5% of MTCs, all using WinBUGS; however, code was infrequently provided (20.6%). More than two-thirds of MTCs (76.5%) also conducted traditional meta-analysis. Methods used to evaluate convergence, heterogeneity and inconsistency were infrequently reported, but from those providing detail, methods appeared varied. MTCs most often used a binary effect measure (85.3%) and ranking of interventions based on probability was common (61.8%), although rarely displayed in a figure (8.8% of MTCs). MTCs were published in 24 different journals with a mean impact factor of 9.20±8.71. While 70.8% of journals imposed limits on word counts and 45.8% limits on the number of tables/figures, online supplements/appendices were allowed in 79.2% of journals. Publication of closed-loop Bayesian MTCs is increasing in frequency, but details regarding their methodology are often poorly described. Efforts in clarifying the appropriate methods and reporting of Bayesian MTCs should be of priority.

Norman Fenton's 45 minute The James Hutton Institute Seminar, Aberdeen 27 September 2023

Norman Fenton's 45 minute The James Hutton Institute Seminar, Aberdeen 27 September 2023

Norman Fenton's 45 minute The James Hutton Institute Seminar, Aberdeen 27 September 2023

This thesis examines three methods for calculating the 100(1- α)% lower confidence limits for the reliability of a K-sized series system. Assuming that each component reliability has a Beta distribution, identical posterior parameters A and B are assigned for each component.

This paper provides a detailed theoretical analysis of methods to approximate the solutions of high-dimensional (>10^6) linear Bayesian problems. An optimal low-rank projection that maximizes the information content of the Bayesian inversion is proposed and efficiently constructed using a scalable randomized SVD algorithm. Useful optimality results are established for the associated posterior error covariance matrix and posterior mean approximations, which are further investigated in a numerical experiment consisting of a large-scale atmospheric tracer transport source-inversion problem. This method proves to be a robust and efficient approach to dimension reduction, as well as a natural framework to analyze the information content of the inversion. Possible extensions of this approach to the non-linear framework in the context of operational numerical weather forecast data assimilation systems based on the incremental 4D-Var technique are also discussed, and a detailed implementation of a new Randomized Incremental Optimal Technique (RIOT) for 4D-Var algorithms leveraging our theoretical results is proposed.

This paper provides a detailed theoretical analysis of methods to approximate the solutions of high-dimensional (>10^6) linear Bayesian problems. An optimal low-rank projection that maximizes the information content of the Bayesian inversion is proposed and efficiently constructed using a scalable randomized SVD algorithm. Useful optimality results are established for the associated posterior error covariance matrix and posterior mean approximations, which are further investigated in a numerical experiment consisting of a large-scale atmospheric tracer transport source-inversion problem. This method proves to be a robust and efficient approach to dimension reduction, as well as a natural framework to analyze the information content of the inversion. Possible extensions of this approach to the non-linear framework in the context of operational numerical weather forecast data assimilation systems based on the incremental 4D-Var technique are also discussed, and a detailed implementation of a new Randomized Incremental Optimal Technique (RIOT) for 4D-Var algorithms leveraging our theoretical results is proposed.

1Thaw

By Edward Thomas

LibriVox volunteers bring you 11 recordings of <em>Thaw</em> by Edward Thomas. This was the weekly poetry project for March 1st, 2009.

LibriVox volunteers bring you 13 recordings of <em>When First I Came Here</em> by Edward Thomas. This was the weekly poetry project for July 19th, 2009.

LibriVox volunteers bring you 7 recordings of Adlestrop by Edward Thomas. This was the Weekly Poetry project for September 20th, 2009.

William Griffith Thomas was a pastor, teacher and co-founder of the Dallas Theological Seminary. This book contains nine devotional commentaries on prayers from Paul's letters to the churches at Thessalonica, Colossi and Ephesus. Thomas is theologically conservative. His commentaries both look at the meaning of the text and apply it to the heart. Summary by MaryAnn.

Edith Matilda Thomas (August 12, 1854 – September 13, 1925) was an American poet who "was one of the first poets to capture successfully the excitement of the modern city." This poem taken from the The Little Book of Modern Verse. 1917.; Jessie B. Rittenhouse, ed. (1869–1948) - Summary by Wikipedia

6What The Pine Trees Said

By Edith M. Thomas

Edith Matilda Thomas was an American poet who "was one of the first poets to capture successfully the excitement of the modern city. (Wikipedia)

7Charley's Aunt

By Brandon Thomas

The girlfriends are coming to visit the chaps at college, but of course they can't stay unless there is a proper chaperone. So what could be more reasonable that getting a friend from the Drama Club to dress up and pretend to be Charley's Aunt? Simple and sure to work! What could go wrong? Howsabout the real aunt arriving? This play has been revived and adapted numerous times including as films, a Broadway musical, and even an opera. (NOTE: the script contains an almost overwhelming number of stage directions by the author telling each actor what emotion to show, where to move, and even how to create some of the visual effects. Because the directions give a good idea of the on-stage pandemonium one would see at a production of this farce, I have chosen to leave the directions in this audio performance.) - Summary by ToddHW <BR><BR>Cast list:<BR> STEPHEN SPETTIGUE, Solicitor, Oxford: <a href="https://librivox.org/reader/12500">Foon</a><br> COLONEL SIR FRANCIS CHESNEY, BART., Late Indian Service: <a href="https://librivox.org/reader/8425">Larry Wilson</a><br> JACK CHESNEY, Graduate at St. Olde's College, Oxford: <a href="https://librivox.org/reader/12431">Tom Daley</a><br> CHARLEY WYKEHAM, Graduate at St. Olde's College, Oxford: <a href="https://librivox.org/reader/11905">Wolfgang Bas</a><br> LORD FANCOURT BABBERLEY, Graduate at St. Olde's College, Oxford: <a href="https://librivox.org/reader/10789">Tomas Peter</a><br> BRASSETT, A College Scout: <a href="https://librivox.org/reader/6754">ToddHW</a><br> DONNA-LUCIA D'ALVADOREZ, From Brazil: <a href="https://librivox.org/reader/10179">Sonia</a><br> AMY SPETTIGUE, Spettigue's Niece: <a href="https://librivox.org/reader/10614">Leanne Yau</a><br> KITTY VERDUN, Spettigue's Ward: <a href="https://librivox.org/reader/6281">Beth Thomas</a><br> ELA DELAHAY: <a href="https://librivox.org/reader/11790">TJ Burns</a><br> Stage Directions: <a href="https://librivox.org/reader/11084">Devorah Allen</a><br> Edited by: <a href="https://librivox.org/reader/6754">ToddHW</a>

8Poems by Edward Thomas

By Edward Thomas

Born in 1878, Thomas published his first book when he was 18. Having married while still at university, he supported his family by writing articles and books, some in the form of what we might call slow travel writing, compiled on walks throughout England and Wales. He came to poetry late, encouraged by Robert Frost, and wrote 144 poems between 1914, and 1917 when he was killed, two years after enlisting, and shortly after arriving in France. <br><br> His poetic life coincided with WW1, and though not a war poet, his is the poetry of loss, of life as it would never be again. What is powerful to the English imagination is his depiction of the fragility of the English countryside. This is inseparable from his deep understanding of the longings and regrets of those who would die. Transience and mortality are at the heart of his work. This is true in one of the country’s favourite poems, to be found on this recording: Adlestrop. He is important to other poets in that, at his best, his poetry is quietly, sometimes coldly, conversational, with a slow beat that takes us with him as he thinks through from line to line, and wraps us in his vision of life and the natural world. (Summary by Judith Brennan)

Jātaka tales are ancient Indian folktales that form a part of Buddhist teaching, telling stories of the Buddha’s past lives. Akin to Aesop’s fables, some strikingly similar, they urge the listener to moral behavior and often, more than that, point a way to real insight. - Summary by Scotty Smith

Two fifteen-year-old boys---the younger of whom may have been fourteen---decide to hunt and trap away from home in the north woods of Maine. A true recounting of their adventures can be found here, though the years that passed before the tale was written down may have added a slight bit of exaggeration. Then again, they set out in the winter of 1875 and all that's written could very well be the complete and honest truth! The listener may wish to listen first to Section 18 of this recording---Notes on Maine Animals---as familiarity with these will enhance an understanding of the memoir.

An explanation of the concept of industrial democracy and its relation to capitalism. (Summary by progressingamerica)

12George Sand

By Bertha Thomas

A Victorian Novelist herself, Bertha Thomas presents a biography of the Life of George Sand - Summary by Christine Rottger

A superior Victorian Romance. Full from bow to stern with secret romance, jealousies and confrontations of culture and cultures. This authors' work contain an abundance of charm and engaging dialogue. - Summary by Christine Rottger

This collection celebrates the marigold in verse from the 17th through the 20th centuries. - Summary by Newgatenovelist

In terms of population, in terms of development and in terms of wealth, Victoria boomed in the 1850s and 1860s due to the Gold Rush. Undeniably, the colony had money to burn. Surprise port visits by the Russian corvette Bogatyr and the Confederate warship Shenandoah in the 1860s awakened in Victoria a deep sense of vulnerability, and Victorians set their minds to a new domestic project – a decades long pursuit of security. That effort would ultimately give rise to extensive coastal fortifications, militia and artillery formations both voluntary and professional, sea mines, and a small but not inconsequential naval flotilla whose backbone would be the formidable ironclad H.M.V.S. Cerberus. Not that these efforts were confined to Victoria alone. The not-infrequently perilous straits of the Mother Country as it navigated the treacherous shoals of European politics and global empire provided ample reasons for insomnia. To varying degrees the other states too had constituted forces, and most made a start at acquiring ‘warships’—an assemblage tending more to the ‘dinky’ side than that term might otherwise suggest. But where naval matters were concerned New South Wales made something of a false start, while Queensland and South Australia simply started late. Whether this is because the issue didn’t resonate as much in the other colonies, or whether Victoria simply had larger resources to sink into an inherently (economically) unproductive project is not immediately obvious. Efforts _were_ made in all the colonies, however and joint defence and a federal fleet would be a not insignificant impetus towards Federation. The Australian colonies would largely abandon armaments acquisition with the depression of the 1890s, but the arrival of Theodore Roosevelt’s Great White Fleet in 1908 exercised popular imagination, and the recently federated Commonwealth of Australia set about acquiring a new navy. This new Royal Australian Navy, lead by the battle-cruiser HMAS Australia, steamed into Sydney Harbour for the first time in late 1913. This new institution would be tested far more strenuously than the naval forces of the unfederated colonies ever were. My appreciation to the [url=” https://www.cerberus.com.au/index.html]”Friends of the Cerberus[/url] for their extensive online resources. - Summary by Alister