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 Ankan, Ankur, author.
 Birmingham, UK : Packt Publishing, 2018.
 Description
 Book — 1 online resource (1 volume) : illustrations
 Summary

 Table of Contents Introduction to Markov Process Hidden Markov Models State Inference: Predicting the states Parameter Inference using Maximum Likelihood Parameter Inference using Bayesian Approach Time Series: Predicting Stock Prices Natural Language Processing: Teaching machines to talk 2DHMM for Image Processing Reinforcement Learning: Teaching a robot to cross a maze.
 (source: Nielsen Book Data)
(source: Nielsen Book Data)
 Ankan, Ankur, author.
 Birmingham, UK : Packt Publishing, 2015.
 Description
 Book — 1 online resource (1 volume) : illustrations.
 Summary

 Cover; Copyright; Credits; About the Authors; About the Reviewers; www.PacktPub.com; Table of Contents; Preface;
 Chapter 1: Bayesian Network Fundamentals; Probability theory; Random variable; Independence and conditional independence; Installing tools; IPython; pgmpy; Representing independencies using pgmpy; Representing joint probability distributions using pgmpy; Conditional probability distribution; Representing CPDs using pgmpy; Graph theory; Nodes and edges; Walk, paths, and trails; Bayesian models; Representation; Factorization of a distribution over a network
 Implementing Bayesian networks using pgmpyBayesian model representation; Reasoning pattern in Bayesian networks; Dseparation; Direct connection; Indirect connection; Relating graphs and distributions; IMAP; IMAP to factorization; CPD representations; Deterministic CPDs; Contextspecific CPDs; Tree CPD; Rule CPD; Summary; Chapter 2: Markov Network Fundamentals; Introducing the Markov network; Parameterizing a Markov network
 factor; Factor operations; Gibbs distributions and Markov networks; The factor graph; Independencies in Markov networks; Constructing graphs from distributions
 Bayesian networks and Markov networksConverting Bayesian models into Markov models; Converting Markov models into Bayesian models; Chordal graphs; Summary; Chapter 3: Inference
 Asking Questions to Models; Inference; Complexity of inference; Variable elimination; Analysis of variable elimination; Finding elimination ordering; Using the chordal graph property of induced graphs; Minimum fill/size/weight/search; Belief propagation; Clique tree; Constructing a clique tree; Message passing; Clique tree calibration; Message passing with division; Factor division
 Querying variables that are not in the same clusterMAP using variable elimination; Factor maximization; MAP using belief propagation; Finding the most probable assignment; Predictions from the model using pgmpy; A comparison of variable elimination and belief propagation; Summary;
 Chapter 4: Approximate Inference; The optimization problem; The energy function; Exact inference as an optimization; The propagation based approximation algorithm; Cluster graph belief propagation; Constructing cluster graphs; Pairwise Markov networks; Bethe cluster graph; Propagation with approximate messages
 Message creationInference with approximate messages; Sumproduct expectation propagation; Belief update propagation; Samplingbased approximate methods; Forward sampling; Conditional probability distribution; Likelihood weighting and importance sampling; Importance sampling; Importance sampling in Bayesian networks; Computing marginal probabilities; Ratio likelihood weighting; Normalized likelihood weighting; Markov chain Monte Carlo methods; Gibbs sampling; Markov chains; The multiple transitioning model; Using a Markov chain; Collapsed particles; Collapsed importance sampling; Summary
 Chapter 5: Model Learning
 Parameter Estimation in Bayesian Networks
(source: Nielsen Book Data)
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