Titlebook: Probabilistic Graphical Models; Principles and Appli Luis Enrique Sucar Textbook 2021Latest edition Springer Nature Switzerland AG 2021 Bay

CULT

書目名稱Probabilistic Graphical Models影響因子(影響力)




書目名稱Probabilistic Graphical Models影響因子(影響力)學(xué)科排名




書目名稱Probabilistic Graphical Models網(wǎng)絡(luò)公開度




書目名稱Probabilistic Graphical Models網(wǎng)絡(luò)公開度學(xué)科排名




書目名稱Probabilistic Graphical Models被引頻次




書目名稱Probabilistic Graphical Models被引頻次學(xué)科排名




書目名稱Probabilistic Graphical Models年度引用




書目名稱Probabilistic Graphical Models年度引用學(xué)科排名




書目名稱Probabilistic Graphical Models讀者反饋




書目名稱Probabilistic Graphical Models讀者反饋學(xué)科排名

Individual

失望昨天

Graph Theoryition of directed and undirected graphs, some basic theoretical graph concepts are introduced, including types of graphs, trajectories and circuits, and graph isomorphism. A section is dedicated to trees, an important type of graph. Some more advanced theoretical graph aspects required for inference

NIB

Bayesian Classifiersl as its main variants: TAN and BAN. Then the semi-naive Bayesian classifier is described. A multidimensional classifier may assign several classes to the same object. Two alternatives for multidimensional classification are analyzed: the multidimensional Bayesian network classifier and the Bayesian

sorbitol

Hidden Markov Modelsns, this chapter focuses on hidden Markov models. The algorithms for solving the basic problems: . and . are presented. Next a description of Gaussian HMMs and several extensions to the basic HMM are given. The chapter concludes with two applications: the “PageRank” procedure used by Google and gest

圖畫文字

Markov Random Fieldse how a Markov random field is represented, including its structure and parameters, with emphasis on regular MRFs. Then, a general stochastic simulation algorithm to find the . configuration of a MRF is described, including some of its main variants. The problem of parameter estimation for a MRF is

神秘

Bayesian Networks: Representation and Inferenceed, including the concept of D-Separation and the independence axioms. With respect to parameter specification, the two main alternatives for a compact representation are described, one based on canonical models and the other on graphical representations. Then the algorithms for probabilistic infere

漫不經(jīng)心

Bayesian Networks: Learninghandle uncertainty in the parameters and missing data; it also includes the basic discretization techniques. After describing the techniques for learning tree and polytree BNs, the two main types of methods for structure learning are described: score and search, and independence tests. We then descr

PAC

stratum-corneum

Decision Graphsrees and their evaluation strategy. Thirdly, influence diagrams are introduced, including three alternative evaluation strategies: transformation to a decision tree, variable elimination and transformation to a Bayesian network. The chapter concludes with two application examples: a decision support