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(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.

This volume contains selections from among the presentations at the Thirteenth International Workshop on Maximum Entropy and Bayesian Methods- MAXENT93 for short- held at the University of California, Santa Barbara (UCSB), August 1-5, 1993. This annual workshop is devoted to the theory and practice of Bayesian probability and the use of the maximum entropy principle in assigning prior probabilities. Like its predecessors, MAXENT93 attracted researchers and scholars representing a wide diversity of disciplines and applications. These included physicists, geophysicists, astronomers, statisticians, engineers, and economists, among others. Indeed Bayesian methods increasingly compel the interest of any who would apply scientific inference. The impressive successes, so evident in the proceedings of the past workshops, when adherence to Bayesian principles replaces popular ad hoc approaches in problems of inference, continue. Many are reported in this volume. It is perhaps indicative of the growing acceptance of Bayesian methods that the most prominent controversy at the thirteenth workshop was not a Bayesian- frequents confrontation but rather a disagreement over the suitability of using an approximation in the Bayesian formalism.

This volume contains selections from among the presentations at the Thirteenth International Workshop on Maximum Entropy and Bayesian Methods- MAXENT93 for short- held at the University of California, Santa Barbara (UCSB), August 1-5, 1993. This annual workshop is devoted to the theory and practice of Bayesian probability and the use of the maximum entropy principle in assigning prior probabilities. Like its predecessors, MAXENT93 attracted researchers and scholars representing a wide diversity of disciplines and applications. These included physicists, geophysicists, astronomers, statisticians, engineers, and economists, among others. Indeed Bayesian methods increasingly compel the interest of any who would apply scientific inference. The impressive successes, so evident in the proceedings of the past workshops, when adherence to Bayesian principles replaces popular ad hoc approaches in problems of inference, continue. Many are reported in this volume. It is perhaps indicative of the growing acceptance of Bayesian methods that the most prominent controversy at the thirteenth workshop was not a Bayesian- frequents confrontation but rather a disagreement over the suitability of using an approximation in the Bayesian formalism.

Partial contents include: (1) AN INTRODUCTION TO MODEL SELECTION USING PROBABILITY THEORY AS LOGIC; (2) HYPERPARAMETERS: OPTIMIZE, OR INTEGRATE OUT?; (3) WHAT BAYES HAS TO SAY ABOUT THE EVIDENCE PROCEDURE; (4) RECONCILING BAYESIAN AND NON-BAYESIAN ANALYSIS; (5) BAYESIAN ROBUSTNESS: A NEW LOOK FROM GEOMETRY; (6) LOCAL POSTERIOR ROBUSTNESS WITH PARAMETRIC PRIORS: MAXIMUM AND AVERAGE SENSITIVITY; (7) BAYESIAN ROBUSTNESS: A NEW LOOK FROM GEOMETRY; (8) LOCAL POSTERIOR ROBUSTNESS WITH PARAMETRIC PRIORS: MAXIMUM AND AVERAGE SENSITIVITY; (9) TREE-STRUCTURED CLUSTERING VIA THE MINIMUM CROSS ENTROPY PRINCIPLE; (10) A SCALE-INVARIANT BAYESIAN METHOD TO SOLVE LINEAR INVERSE PROBLEMS; (11) MAXIMUM ENTROPY SIGNAL TRANSMISSION; (12) MAXIMUM QUANTUM ENTROPY FOR CLASSICAL DENSITY FUNCTIONS; (13) SMOOTHING IN MAXIMUM QUANTUM ENTROPY; (14) DENSITY ESTIMATION BY MAXIMUM QUANTUM ENTROPY; (15) A BAYESIAN GENETIC ALGORITHM FOR CALCULATING MAXIMUM ENTROPY DISTRIBUTIONS; (16) A MATHEMATICIAN PACKAGE FOR SYMBOLIC BAYESIAN CALCULATIONS; (17) A MULTICRITERION EVALUATION OF THE MEMSYS5 PROGRAM FOR PET; (18) PARALLEL MAXIMUM ENTROPY RECONSTRUCTION OF PET IMAGES; (19) BAYESIAN NON-LINEAR MODELING FOR THE PREDICTION COMPETITION; (20) BAYESIAN MO%MODELING AND CLASSIFICATION OF NEURAL SIGNALS; (21) PIXON-BASED IMAGE RECONSTRUCTION; (22) SUPER-RESOLVED SURFACE RECONSTRUCTION FROM MULTIPLE IMAGES; (23) BAYESIAN ANALYSIS OF LINEAR PHASED-ARRAY RADAR; (24) NEURAL NETWORK IMAGE DECONVOLUTION; (25) BAYESIAN RESOLUTION OF CLOSELY SPACED OBJECT; (26) ULTRASONIC IMAGE IMPROVEMENT THROUGH THE USE OF BAYESIAN PRIORS WHICH ARE BASED ON ADJACENT SCANNED TRACES; and (27) APPLICATION OF MAXENT TO INVERSE PHOTOEMISSION SPECTROSCOPY

Partial contents include: (1) AN INTRODUCTION TO MODEL SELECTION USING PROBABILITY THEORY AS LOGIC; (2) HYPERPARAMETERS: OPTIMIZE, OR INTEGRATE OUT?; (3) WHAT BAYES HAS TO SAY ABOUT THE EVIDENCE PROCEDURE; (4) RECONCILING BAYESIAN AND NON-BAYESIAN ANALYSIS; (5) BAYESIAN ROBUSTNESS: A NEW LOOK FROM GEOMETRY; (6) LOCAL POSTERIOR ROBUSTNESS WITH PARAMETRIC PRIORS: MAXIMUM AND AVERAGE SENSITIVITY; (7) BAYESIAN ROBUSTNESS: A NEW LOOK FROM GEOMETRY; (8) LOCAL POSTERIOR ROBUSTNESS WITH PARAMETRIC PRIORS: MAXIMUM AND AVERAGE SENSITIVITY; (9) TREE-STRUCTURED CLUSTERING VIA THE MINIMUM CROSS ENTROPY PRINCIPLE; (10) A SCALE-INVARIANT BAYESIAN METHOD TO SOLVE LINEAR INVERSE PROBLEMS; (11) MAXIMUM ENTROPY SIGNAL TRANSMISSION; (12) MAXIMUM QUANTUM ENTROPY FOR CLASSICAL DENSITY FUNCTIONS; (13) SMOOTHING IN MAXIMUM QUANTUM ENTROPY; (14) DENSITY ESTIMATION BY MAXIMUM QUANTUM ENTROPY; (15) A BAYESIAN GENETIC ALGORITHM FOR CALCULATING MAXIMUM ENTROPY DISTRIBUTIONS; (16) A MATHEMATICIAN PACKAGE FOR SYMBOLIC BAYESIAN CALCULATIONS; (17) A MULTICRITERION EVALUATION OF THE MEMSYS5 PROGRAM FOR PET; (18) PARALLEL MAXIMUM ENTROPY RECONSTRUCTION OF PET IMAGES; (19) BAYESIAN NON-LINEAR MODELING FOR THE PREDICTION COMPETITION; (20) BAYESIAN MO%MODELING AND CLASSIFICATION OF NEURAL SIGNALS; (21) PIXON-BASED IMAGE RECONSTRUCTION; (22) SUPER-RESOLVED SURFACE RECONSTRUCTION FROM MULTIPLE IMAGES; (23) BAYESIAN ANALYSIS OF LINEAR PHASED-ARRAY RADAR; (24) NEURAL NETWORK IMAGE DECONVOLUTION; (25) BAYESIAN RESOLUTION OF CLOSELY SPACED OBJECT; (26) ULTRASONIC IMAGE IMPROVEMENT THROUGH THE USE OF BAYESIAN PRIORS WHICH ARE BASED ON ADJACENT SCANNED TRACES; and (27) APPLICATION OF MAXENT TO INVERSE PHOTOEMISSION SPECTROSCOPY

Partial contents include: (1) AN INTRODUCTION TO MODEL SELECTION USING PROBABILITY THEORY AS LOGIC; (2) HYPERPARAMETERS: OPTIMIZE, OR INTEGRATE OUT?; (3) WHAT BAYES HAS TO SAY ABOUT THE EVIDENCE PROCEDURE; (4) RECONCILING BAYESIAN AND NON-BAYESIAN ANALYSIS; (5) BAYESIAN ROBUSTNESS: A NEW LOOK FROM GEOMETRY; (6) LOCAL POSTERIOR ROBUSTNESS WITH PARAMETRIC PRIORS: MAXIMUM AND AVERAGE SENSITIVITY; (7) BAYESIAN ROBUSTNESS: A NEW LOOK FROM GEOMETRY; (8) LOCAL POSTERIOR ROBUSTNESS WITH PARAMETRIC PRIORS: MAXIMUM AND AVERAGE SENSITIVITY; (9) TREE-STRUCTURED CLUSTERING VIA THE MINIMUM CROSS ENTROPY PRINCIPLE; (10) A SCALE-INVARIANT BAYESIAN METHOD TO SOLVE LINEAR INVERSE PROBLEMS; (11) MAXIMUM ENTROPY SIGNAL TRANSMISSION; (12) MAXIMUM QUANTUM ENTROPY FOR CLASSICAL DENSITY FUNCTIONS; (13) SMOOTHING IN MAXIMUM QUANTUM ENTROPY; (14) DENSITY ESTIMATION BY MAXIMUM QUANTUM ENTROPY; (15) A BAYESIAN GENETIC ALGORITHM FOR CALCULATING MAXIMUM ENTROPY DISTRIBUTIONS; (16) A MATHEMATICIAN PACKAGE FOR SYMBOLIC BAYESIAN CALCULATIONS; (17) A MULTICRITERION EVALUATION OF THE MEMSYS5 PROGRAM FOR PET; (18) PARALLEL MAXIMUM ENTROPY RECONSTRUCTION OF PET IMAGES; (19) BAYESIAN NON-LINEAR MODELING FOR THE PREDICTION COMPETITION; (20) BAYESIAN MO%MODELING AND CLASSIFICATION OF NEURAL SIGNALS; (21) PIXON-BASED IMAGE RECONSTRUCTION; (22) SUPER-RESOLVED SURFACE RECONSTRUCTION FROM MULTIPLE IMAGES; (23) BAYESIAN ANALYSIS OF LINEAR PHASED-ARRAY RADAR; (24) NEURAL NETWORK IMAGE DECONVOLUTION; (25) BAYESIAN RESOLUTION OF CLOSELY SPACED OBJECT; (26) ULTRASONIC IMAGE IMPROVEMENT THROUGH THE USE OF BAYESIAN PRIORS WHICH ARE BASED ON ADJACENT SCANNED TRACES; and (27) APPLICATION OF MAXENT TO INVERSE PHOTOEMISSION SPECTROSCOPY

Partial contents include: (1) AN INTRODUCTION TO MODEL SELECTION USING PROBABILITY THEORY AS LOGIC; (2) HYPERPARAMETERS: OPTIMIZE, OR INTEGRATE OUT?; (3) WHAT BAYES HAS TO SAY ABOUT THE EVIDENCE PROCEDURE; (4) RECONCILING BAYESIAN AND NON-BAYESIAN ANALYSIS; (5) BAYESIAN ROBUSTNESS: A NEW LOOK FROM GEOMETRY; (6) LOCAL POSTERIOR ROBUSTNESS WITH PARAMETRIC PRIORS: MAXIMUM AND AVERAGE SENSITIVITY; (7) BAYESIAN ROBUSTNESS: A NEW LOOK FROM GEOMETRY; (8) LOCAL POSTERIOR ROBUSTNESS WITH PARAMETRIC PRIORS: MAXIMUM AND AVERAGE SENSITIVITY; (9) TREE-STRUCTURED CLUSTERING VIA THE MINIMUM CROSS ENTROPY PRINCIPLE; (10) A SCALE-INVARIANT BAYESIAN METHOD TO SOLVE LINEAR INVERSE PROBLEMS; (11) MAXIMUM ENTROPY SIGNAL TRANSMISSION; (12) MAXIMUM QUANTUM ENTROPY FOR CLASSICAL DENSITY FUNCTIONS; (13) SMOOTHING IN MAXIMUM QUANTUM ENTROPY; (14) DENSITY ESTIMATION BY MAXIMUM QUANTUM ENTROPY; (15) A BAYESIAN GENETIC ALGORITHM FOR CALCULATING MAXIMUM ENTROPY DISTRIBUTIONS; (16) A MATHEMATICIAN PACKAGE FOR SYMBOLIC BAYESIAN CALCULATIONS; (17) A MULTICRITERION EVALUATION OF THE MEMSYS5 PROGRAM FOR PET; (18) PARALLEL MAXIMUM ENTROPY RECONSTRUCTION OF PET IMAGES; (19) BAYESIAN NON-LINEAR MODELING FOR THE PREDICTION COMPETITION; (20) BAYESIAN MO%MODELING AND CLASSIFICATION OF NEURAL SIGNALS; (21) PIXON-BASED IMAGE RECONSTRUCTION; (22) SUPER-RESOLVED SURFACE RECONSTRUCTION FROM MULTIPLE IMAGES; (23) BAYESIAN ANALYSIS OF LINEAR PHASED-ARRAY RADAR; (24) NEURAL NETWORK IMAGE DECONVOLUTION; (25) BAYESIAN RESOLUTION OF CLOSELY SPACED OBJECT; (26) ULTRASONIC IMAGE IMPROVEMENT THROUGH THE USE OF BAYESIAN PRIORS WHICH ARE BASED ON ADJACENT SCANNED TRACES; and (27) APPLICATION OF MAXENT TO INVERSE PHOTOEMISSION SPECTROSCOPY

Partial contents include: (1) AN INTRODUCTION TO MODEL SELECTION USING PROBABILITY THEORY AS LOGIC; (2) HYPERPARAMETERS: OPTIMIZE, OR INTEGRATE OUT?; (3) WHAT BAYES HAS TO SAY ABOUT THE EVIDENCE PROCEDURE; (4) RECONCILING BAYESIAN AND NON-BAYESIAN ANALYSIS; (5) BAYESIAN ROBUSTNESS: A NEW LOOK FROM GEOMETRY; (6) LOCAL POSTERIOR ROBUSTNESS WITH PARAMETRIC PRIORS: MAXIMUM AND AVERAGE SENSITIVITY; (7) BAYESIAN ROBUSTNESS: A NEW LOOK FROM GEOMETRY; (8) LOCAL POSTERIOR ROBUSTNESS WITH PARAMETRIC PRIORS: MAXIMUM AND AVERAGE SENSITIVITY; (9) TREE-STRUCTURED CLUSTERING VIA THE MINIMUM CROSS ENTROPY PRINCIPLE; (10) A SCALE-INVARIANT BAYESIAN METHOD TO SOLVE LINEAR INVERSE PROBLEMS; (11) MAXIMUM ENTROPY SIGNAL TRANSMISSION; (12) MAXIMUM QUANTUM ENTROPY FOR CLASSICAL DENSITY FUNCTIONS; (13) SMOOTHING IN MAXIMUM QUANTUM ENTROPY; (14) DENSITY ESTIMATION BY MAXIMUM QUANTUM ENTROPY; (15) A BAYESIAN GENETIC ALGORITHM FOR CALCULATING MAXIMUM ENTROPY DISTRIBUTIONS; (16) A MATHEMATICIAN PACKAGE FOR SYMBOLIC BAYESIAN CALCULATIONS; (17) A MULTICRITERION EVALUATION OF THE MEMSYS5 PROGRAM FOR PET; (18) PARALLEL MAXIMUM ENTROPY RECONSTRUCTION OF PET IMAGES; (19) BAYESIAN NON-LINEAR MODELING FOR THE PREDICTION COMPETITION; (20) BAYESIAN MO%MODELING AND CLASSIFICATION OF NEURAL SIGNALS; (21) PIXON-BASED IMAGE RECONSTRUCTION; (22) SUPER-RESOLVED SURFACE RECONSTRUCTION FROM MULTIPLE IMAGES; (23) BAYESIAN ANALYSIS OF LINEAR PHASED-ARRAY RADAR; (24) NEURAL NETWORK IMAGE DECONVOLUTION; (25) BAYESIAN RESOLUTION OF CLOSELY SPACED OBJECT; (26) ULTRASONIC IMAGE IMPROVEMENT THROUGH THE USE OF BAYESIAN PRIORS WHICH ARE BASED ON ADJACENT SCANNED TRACES; and (27) APPLICATION OF MAXENT TO INVERSE PHOTOEMISSION SPECTROSCOPY