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1Stochastic Processes With Learning Properties

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2Stochastic Processes : 10th Seminar : Selected Papers

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3Stationary Stochastic Processes

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4Stochastic Processes

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5Stochastic Processes And Their Applications 2003: Vol 105 Index

Stochastic Processes and Their Applications 2003: Volume 105 , Issue Index. Digitized from IA1652405-03 . Previous issue: sim_stochastic-processes-and-their-applications_2003-04_104_2_0 . Next issue: sim_stochastic-processes-and-their-applications_2003-05_105_1_0 .

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6Stochastic Processes

Stochastic Processes Kaddour Najim Ensi Ikonen Ait-Kadi Daoud 2004

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  • Title: Stochastic Processes

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7Stochastic_processes_lec9

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8Stochastic_processes_sec1

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9Stochastic Processes In Demography And Their Computer Implementation

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10Stochastic Processes

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Includes bibliographical references and index

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11Stochastic Processes And Their Applications

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Includes bibliographical references and index

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12Some Properties Of Strongly Continuous Stochastic Processes

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Includes bibliographical references and index

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13Models For Behavior : Stochastic Processes In Psychology

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Includes bibliographical references and index

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14BSP 2014 - Introduction To Applied Probability And Stochastic Processes

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Trimester 2 2018/2019

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15DTIC ADA119878: Fundamental Research In Mathematical Statistics And Probability; Stochastic Processes.

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Brief description of research findings for the period 1 April 1976 to 30 June 1982: The results refer to a number of topics in the construction of tests or estimates with applications to selection procedures for studies, weather modification, tests of fit for logistic models competing risks, identifiability in Markov processes. A list of papers published and a list of Ph.D. granted are appended. (Author)

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16Stochastic Processes And Their Applications 1991: Vol 39 Index

Stochastic Processes and Their Applications 1991: Volume 39 , Issue Index. Digitized from IA1652405-03 . Previous issue: sim_stochastic-processes-and-their-applications_1991-08_38_2_0 . Next issue: sim_stochastic-processes-and-their-applications_1991-10_39_1_0 .

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17Stochastic Processes And Their Applications 1992: Vol 42 Index

Stochastic Processes and Their Applications 1992: Volume 42 , Issue Index. Digitized from IA1653419-02 . Previous issue: sim_stochastic-processes-and-their-applications_1992-06_41_2 . Next issue: sim_stochastic-processes-and-their-applications_1992-08_42_1 .

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18Stochastic Processes And Their Applications 1992: Vol 43 Index

Stochastic Processes and Their Applications 1992: Volume 43 , Issue Index. Digitized from IA1653419-02 . Previous issue: sim_stochastic-processes-and-their-applications_1992-09_42_2 . Next issue: sim_stochastic-processes-and-their-applications_1992-11_43_1 .

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19Stochastic Processes And Their Applications 1993: Vol 48 Index

Stochastic Processes and Their Applications 1993: Volume 48 , Issue Index. Digitized from IA1652405-03 . Previous issue: sim_stochastic-processes-and-their-applications_1993-09_47_2_0 . Next issue: sim_stochastic-processes-and-their-applications_1993-10_48_1_0 .

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20DTIC ADA097514: Partial Characterizations Of Completely Nondeterministic Stochastic Processes.

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A discrete weakly stationary Gaussian stochastic process x(t), is completely nondeterministic if no non-trivial set from the sigma-algebra generated by x(t):t 0 lies in the sigma-algebra generated by x(t):t or = 0. Levinson and McKean essentially showed that a necessary and sufficient condition for complete non-determinism is that the spectrum of the process is given by absolute value of H squared where h is an outer function in the Hardy space, H superscript + 2, of the unit circle in -C with the property that h/h bar uniquely determines the outer function h up to an arbitrary constant. In this paper we consider several characterizations of complete non-determinism in terms of the geometry of the unit ball of the Hardy space H superscript + 1 and in terms of Hankel operators, and pose an open problem. (Author)

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21Stochastic Processes And Their Applications 1995: Vol 57 Index

Stochastic Processes and Their Applications 1995: Volume 57 , Issue Index. Digitized from IA1652405-03 . Previous issue: sim_stochastic-processes-and-their-applications_1995-04_56_2_0 . Next issue: sim_stochastic-processes-and-their-applications_1995-05_57_1_0 .

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22DTIC ADA093633: Smoothing Estimation Of Stochastic Processes. Part I. Change Of Initial Condition Formulae.

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Recently a great amount of attention has been focused on various algorithms for solving the smoothing problem of linear estimation theory. This work is the first part of a two part investigation of these algorithms. In Part I it is shown how change of initial condition (CIC) or partitioning formulae hold in a very general setting (the CIC problem is shown to involve fixed rank perturbation in matrix inversion). In Part II the nature of the two-filter algorithms is explored by providing a simple derivation that shows to what extent the formulae hold generally and so reveals exactly how a wide sense Markovian assumption is necessary for their full utility. The remainder of the paper is structured as follows. Section I contains a discussion of CIC formulae for discrete observations. Section II concerns CIC formulae for continuous observations (actually the formulae are the same). Section III discusses the relation with other work.

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23Stochastic_processes_sec3

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24The Stochastic Modeling Of Elementary Psychological Processes

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25Stochastic Processes In Polymeric Fluids : Tools And Examples For Developing Simulation Algorithms

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26Stationary And Related Stochastic Processes; Sample Function Properties And Their Applications

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27Topics In Spatial Stochastic Processes : Lectures Given At The C.I.M.E. Summer School Held In Martina Franca, Italy, July 1-8, 2001

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28Applied Stochastic Processes

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29Stochastic Flows For L\'evy Processes With H\"{o}lder Drifts

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In this paper we study the following stochastic differential equation (SDE) in ${\mathbb R}^d$: $$ \mathrm{d} X_t= \mathrm{d} Z_t + b(t, X_t)\mathrm{d} t, \quad X_0=x, $$ where $Z$ is a L\'evy process. We show that for a large class of L\'evy processes ${Z}$ and H\"older continuous drift $b$, the SDE above has a unique strong solution for every starting point $x\in{\mathbb R}^d$. Moreover, these strong solutions form a $C^1$-stochastic flow. As a consequence, we show that, when ${Z}$ is an $\alpha$-stable-type L\'evy process with $\alpha\in (0, 2)$ and $b$ is bounded and $\beta$-H\"older continuous with $\beta\in (1- {\alpha}/{2},1)$, the SDE above has a unique strong solution. When $\alpha \in (0, 1)$, this in particular solves an open problem from Priola \cite{Pr1}. Moreover, we obtain a Bismut type derivative formula for $\nabla {\mathbb E}_x f(X_t)$ when ${Z}$ is a subordinate Brownian motion. To study the SDE above, we first study the following nonlocal parabolic equation with H\"older continuous $b$ and $f$: $$ \partial_t u+{\mathscr L} u+b\cdot \nabla u+f=0,\quad u(1, \cdot )=0, $$ where $\mathscr L$ is the generator of the L\'evy process ${Z}$.

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30Heat Processes In Non-equilibrium Stochastic Systems

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This thesis is devoted to the theoretical study of slow thermodynamic processes in non-equilibrium stochastic systems. Its main result is a physically and mathematically consistent construction of relevant thermodynamic quantities in the quasistatic limit for a large class of non-equilibrium models. As an application of general methods a natural non-equilibrium generalization of heat capacity is introduced and its properties are analyzed in detail, including an anomalous far-from-equilibrium behavior. The developed methods are further applied to the related problem of time-scale separation where they enable to describe the effective dynamics of both slow and fast degrees of freedom in a more precise way.

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31Strategy Complexity Of Finite-horizon Markov Decision Processes And Simple Stochastic Games

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Markov decision processes (MDPs) and simple stochastic games (SSGs) provide a rich mathematical framework to study many important problems related to probabilistic systems. MDPs and SSGs with finite-horizon objectives, where the goal is to maximize the probability to reach a target state in a given finite time, is a classical and well-studied problem. In this work we consider the strategy complexity of finite-horizon MDPs and SSGs. We show that for all $\epsilon>0$, the natural class of counter-based strategies require at most $\log \log (\frac{1}{\epsilon}) + n+1$ memory states, and memory of size $\Omega(\log \log (\frac{1}{\epsilon}) + n)$ is required. Thus our bounds are asymptotically optimal. We then study the periodic property of optimal strategies, and show a sub-exponential lower bound on the period for optimal strategies.

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32Space And Time Inversions Of Stochastic Processes And Kelvin Transform

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Let $X$ be a standard Markov process. We prove that a space inversion property of $X$ implies the existence of a Kelvin transform of $X$-harmonic, excessive and operator-harmonic functions and that the inversion property is inherited by Doob $h$-transforms. We determine new classes of processes having space inversion properties amongst transient processes {satisfying the} time inversion property. {For these processes, some explicit inversions, which are often not the spherical ones, and excessive functions are given explicitly.} We treat in details the examples of free scaled power Bessel processes, non-colliding Bessel particles, Wishart processes, Gaussian Ensemble and Dyson Brownian Motion.

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33Quantum Stochastic Processes In Two Dimensional Space-time

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Several stochastic processes with virtual particles in two dimensional space-time are presented whose mean field equations coincide with Schr\"odinger, Dirac, Klein-Gordon and the quantum mechanic equation for a photon. These processes could be used to detect discrete space-time features at the Planck scale.

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34Analysis Of Stochastic Fluid Queues Driven By Local Time Processes

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We consider a stochastic fluid queue served by a constant rate server and driven by a process which is the local time of a certain Markov process. Such a stochastic system can be used as a model in a priority service system, especially when the time scales involved are fast. The input (local time) in our model is always singular with respect to the Lebesgue measure which in many applications is ``close'' to reality. We first discuss how to rigorously construct the (necessarily) unique stationary version of the system under some natural stability conditions. We then consider the distribution of performance steady-state characteristics, namely, the buffer content, the idle period and the busy period. These derivations are much based on the fact that the inverse of the local time of a Markov process is a L\'evy process (a subordinator) hence making the theory of L\'evy processes applicable. Another important ingredient in our approach is the Palm calculus coming from the point process point of view.

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35Wavelet Entropy Of Stochastic Processes

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We compare two different definitions for the wavelet entropy associated to stochastic processes. The first one, the Normalized Total Wavelet Entropy (NTWS) family [Phys. Rev. E 57 (1998) 932; J. Neuroscience Method 105 (2001) 65; Physica A (2005) in press] and a second introduced by Tavares and Lucena [Physica A 357 (2005)~71]. In order to understand their advantages and disadvantages, exact results obtained for fractional Gaussian noise (-1

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36Stochastic Processes In Turbulent Transport

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This is a set of four lectures devoted to simple ideas about turbulent transport, a ubiquitous non-equilibrium phenomenon. In the course similar to that given by the author in 2006 in Warwick [45], we discuss lessons which have been learned from naive models of turbulent mixing that employ simple random velocity ensembles and study related stochastic processes. In the first lecture, after a brief reminder of the turbulence phenomenology, we describe the intimate relation between the passive advection of particles and fields by hydrodynamical flows. The second lecture is devoted to some useful tools of the multiplicative ergodic theory for random dynamical systems. In the third lecture, we apply these tools to the example of particle flows in the Kraichnan ensemble of smooth velocities that mimics turbulence at intermediate Reynolds numbers. In the fourth lecture, we extends the discussion of particle flows to the case of non-smooth Kraichnan velocities that model fully developed turbulence. We stress the unconventional aspects of particle flows that appear in this regime and lead to phase transitions in the presence of compressibility. The intermittency of scalar fields advected by fully turbulent velocities and the scenario linking it to hidden statistical conservation laws of multi-particle flows are briefly explained.

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37A Bernstein-type Inequality For Stochastic Processes Of Quadratic Forms Of Gaussian Variables

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We introduce a Bernstein-type inequality which serves to uniformly control quadratic forms of gaussian variables. The latter can for example be used to derive sharp model selection criteria for linear estimation in linear regression and linear inverse problems via penalization, and we do not exclude that its scope of application can be made even broader.

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38Stochastic Processes And Their Applications 1995: Vol 57 Index

Stochastic Processes and Their Applications 1995: Volume 57 , Issue Index. Digitized from IA1653419-05 . Previous issue: sim_stochastic-processes-and-their-applications_1995-04_56_2 . Next issue: sim_stochastic-processes-and-their-applications_1995-05_57_1 .

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39Stochastic Processes And Their Applications 1996: Vol 63 Index

Stochastic Processes and Their Applications 1996: Volume 63 , Issue Index. Digitized from IA1653419-05 . Previous issue: sim_stochastic-processes-and-their-applications_1996-04_62_2 . Next issue: sim_stochastic-processes-and-their-applications_1996-10_63_1 .

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40Stochastic Processes And Their Applications 1997: Vol 66 Index

Stochastic Processes and Their Applications 1997: Volume 66 , Issue Index. Digitized from IA1652405-03 . Previous issue: sim_stochastic-processes-and-their-applications_1996-12-27_65_2_0 . Next issue: sim_stochastic-processes-and-their-applications_1997-02-01_66_1_0 .

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41Stochastic Processes And Their Applications 1997: Vol 67 Index

Stochastic Processes and Their Applications 1997: Volume 67 , Issue Index. Digitized from IA1653419-05 . Previous issue: sim_stochastic-processes-and-their-applications_1997-03-28_66_2 . Next issue: sim_stochastic-processes-and-their-applications_1997-04-28_67_1 .

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42Stochastic Processes And Their Applications 1997: Vol 70 Index

Stochastic Processes and Their Applications 1997: Volume 70 , Issue Index. Digitized from IA1653419-05 . Previous issue: sim_stochastic-processes-and-their-applications_1997-09-01_69_2 . Next issue: sim_stochastic-processes-and-their-applications_1997-10-01_70_1 .

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43Stochastic Processes And Their Applications 2003: Vol 103 Index

Stochastic Processes and Their Applications 2003: Volume 103 , Issue Index. Digitized from IA1652405-03 . Previous issue: sim_stochastic-processes-and-their-applications_1997-12-15_72_2_0 . Next issue: sim_stochastic-processes-and-their-applications_2003-01_103_1_0 .

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44Stochastic Processes And Their Applications 2003: Vol 104 Index

Stochastic Processes and Their Applications 2003: Volume 104 , Issue Index. Digitized from IA1652405-03 . Previous issue: sim_stochastic-processes-and-their-applications_2003-02_103_2_0 . Next issue: sim_stochastic-processes-and-their-applications_2003-03_104_1_0 .

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45Stochastic Processes And Their Applications 1990: Vol 36 Index

Stochastic Processes and Their Applications 1990: Volume 36 , Issue Index. Digitized from IA1652405-03 . Previous issue: sim_stochastic-processes-and-their-applications_1990-08_35_2_0 . Next issue: sim_stochastic-processes-and-their-applications_1990-10_36_1_0 .

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46Stochastic Processes And Their Applications 1994: Vol 50 Index

Stochastic Processes and Their Applications 1994: Volume 50 , Issue Index. Digitized from IA1653419-05 . Previous issue: sim_stochastic-processes-and-their-applications_1994-02_49_2 . Next issue: sim_stochastic-processes-and-their-applications_1994-03_50_1 .

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47Stochastic Point Processes And Their Applications

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Stochastic Processes and Their Applications 1994: Volume 50 , Issue Index. Digitized from IA1653419-05 . Previous issue: sim_stochastic-processes-and-their-applications_1994-02_49_2 . Next issue: sim_stochastic-processes-and-their-applications_1994-03_50_1 .

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48Stochastic Processes With Applications

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Stochastic Processes and Their Applications 1994: Volume 50 , Issue Index. Digitized from IA1653419-05 . Previous issue: sim_stochastic-processes-and-their-applications_1994-02_49_2 . Next issue: sim_stochastic-processes-and-their-applications_1994-03_50_1 .

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  • Title: ➤  Stochastic Processes With Applications
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49Stochastic Processes In Physics And Chemistry

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Stochastic Processes and Their Applications 1994: Volume 50 , Issue Index. Digitized from IA1653419-05 . Previous issue: sim_stochastic-processes-and-their-applications_1994-02_49_2 . Next issue: sim_stochastic-processes-and-their-applications_1994-03_50_1 .

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  • Title: ➤  Stochastic Processes In Physics And Chemistry
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50Stochastic Calculus For Fractional Brownian Motion And Related Processes

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Stochastic Processes and Their Applications 1994: Volume 50 , Issue Index. Digitized from IA1653419-05 . Previous issue: sim_stochastic-processes-and-their-applications_1994-02_49_2 . Next issue: sim_stochastic-processes-and-their-applications_1994-03_50_1 .

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  • Title: ➤  Stochastic Calculus For Fractional Brownian Motion And Related Processes
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  • Language: English

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The book is available for download in "texts" format, the size of the file-s is: 853.13 Mbs, the file-s for this book were downloaded 49 times, the file-s went public at Thu Aug 11 2022.

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