Probabilistic Graphical Models: Principles and Applications - Couverture souple
Livre 48 sur 86: Advances in Computer Vision and Pattern Recognition
Synopsis
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
Les informations fournies dans la section « Synopsis » peuvent faire référence à une autre édition de ce titre.
