Probabilistic Graphical Models: Principles and Applications - Tapa blanda

Sinopsis

Part I: Fundamentals

Introduction

Probability Theory

Graph Theory

Part II: Probabilistic Models

Bayesian Classifiers

Hidden Markov Models

Markov Random Fields

Bayesian Networks: Representation and Inference

Bayesian Networks: Learning

Dynamic and Temporal Bayesian Networks

Part III: Decision Models

Decision Graphs

Markov Decision Processes

Part IV: Relational and Causal Models

Relational Probabilistic Graphical Models

Graphical Causal Models

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