Probabilistic Graphical Models
portes grátis
Probabilistic Graphical Models
Principles and Applications
Sucar, Luis Enrique
Springer Nature Switzerland AG
12/2021
355
Mole
Inglês
9783030619459
15 a 20 dias
587
Descrição não disponível.
Introduction.- Probability Theory.- Graph Theory.- Bayesian Classifiers.- Hidden Markov Models.- Markov Random Fields.- Bayesian Networks: Representation and Inference.- Bayesian Networks: Learning.- Dynamic and Temporal Bayesian Networks.- Decision Graphs.- Markov Decision Processes.- Partially Observable Markov Decision Processes.- Relational Probabilistic Graphical Models.- Graphical Causal Models.- Causal Discovery.- Deep Learning and Graphical Models.- A Python Library for Inference and Learning.- Glossary.- Index
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Bayesian Classifiers;Bayesian Networks;Decision Networks;Hidden Markov Models;Influence Diagrams;Learning Graphical Models;Markov Decision Processes;Markov Random Fields;Probabilistic Graphical Models;Probabilistic Inference
Introduction.- Probability Theory.- Graph Theory.- Bayesian Classifiers.- Hidden Markov Models.- Markov Random Fields.- Bayesian Networks: Representation and Inference.- Bayesian Networks: Learning.- Dynamic and Temporal Bayesian Networks.- Decision Graphs.- Markov Decision Processes.- Partially Observable Markov Decision Processes.- Relational Probabilistic Graphical Models.- Graphical Causal Models.- Causal Discovery.- Deep Learning and Graphical Models.- A Python Library for Inference and Learning.- Glossary.- Index
Este título pertence ao(s) assunto(s) indicados(s). Para ver outros títulos clique no assunto desejado.
