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Emmanuel Mazer, Juan-Manuel Ahuactzin, Kamel Mekhnacha, Pierre Bessiere
Taylor & Francis Ltd
Date of Publication
Computing: Textbooks & Study Guides
eBay Product ID (ePID)
Country of Publication
Chapman & Hall/CRC
61 black & white illustrations, 5 black & white tables
Unsewn / adhesive bound
Table Of Contents
Introduction Probability an alternative to logic A need for a new computing paradigm A need for a new modeling methodology A need for new inference algorithms A need for a new programming language and new hardware A place for numerous controversies Running real programs as exercises Bayesian Programming Principles Basic Concepts Variable Probability The normalization postulate Conditional probability Variable conjunction The conjunction postulate (Bayes theorem) Syllogisms The marginalization rule Joint distribution and questions Decomposition Parametric forms Identification Specification = variables + decomposition + parametric forms Description = specification + identification Question Bayesian program = description + question Results Incompleteness and Uncertainty Observing a water treatment unit Lessons, comments, and notes Description = Specification + Identification Pushing objects and following contours Description of a water treatment unit Lessons, comments, and notes The Importance of Conditional Independence Water treatment center Bayesian model (continuation) Description of the water treatment center Lessons, comments, and notes Bayesian Program = Description + Question Water treatment center Bayesian model (end) Forward simulation of a single unit Forward simulation of the water treatment center Control of the water treatment center Diagnosis Lessons, comments, and notes Bayesian Programming Cookbook Information Fusion Naive Bayes sensor fusion Relaxing the conditional independence fundamental hypothesis Classification Ancillary clues Sensor fusion with false alarm Inverse programming Bayesian Programming with Coherence Variables Basic example with Boolean variables Basic example with discrete variables Checking the semantic of LAMBDA Information fusion revisited using coherence variables Reasoning with soft evidence Switch Cycles Bayesian Programming Subroutines The sprinkler model Calling subroutines conditioned by values Water treatment center revisited (final) Fusion of subroutines Superposition Bayesian Programming Conditional Statement Bayesian if-then-else Behavior recognition Mixture of models and model recognition Bayesian Programming Iteration Generic iteration Generic Bayesian filters Markov localization Bayesian Programming Formalism and Algorithms Bayesian Programming Formalism Logical propositions Probability of a proposition Normalization and conjunction postulates Disjunction rule for propositions Discrete variables Variable conjunction Probability on variables Conjunction rule for variables Normalization rule for variables Marginalization rule Bayesian program Description Specification Questions Inference Bayesian Models Revisited General purpose probabilistic models Engineering-oriented probabilistic models Cognitive-oriented probabilistic models Bayesian Inference Algorithms Revisited Stating the problem Symbolic computation Numerical computation: General sampling algorithms for approximate Bayesian inference Approximate inference in ProBT Bayesian Learning Revisited Parameter identification Expectation-Maximization (EM) Learning structure of Bayesian networks Frequently Asked Questions and Frequently Argued Matter Frequently Asked Question and Frequently Argued Matter Alternative Bayesian inference engines Bayesian programming applications Bayesian programming vs. Bayesian networks Bayesian programming vs. Bayesian modeling Bayesian programming vs. possibility theories Bayesian programming vs. probabilistic programming Computational complexity of Bayesian inference Cox theorem Discrete vs. continuous variables Incompleteness irreducibility Maximum entropy principle justifications Noise or ignorance? Objectivism vs. subjectivism controversy and the mind projection fallacy Unknown distribution Gloss
Pierre Bessiere is with CNRS, the French National Centre for Scientific Research. Juan-Manuel Ahuactzin, Kamel Mekhnacha, and Emmanuel Mazer are with Probayes Inc., France.