CULT
发表于 2025-3-21 17:11:23
书目名称Probabilistic Graphical Models影响因子(影响力)<br> http://impactfactor.cn/if/?ISSN=BK0756795<br><br> <br><br>书目名称Probabilistic Graphical Models影响因子(影响力)学科排名<br> http://impactfactor.cn/ifr/?ISSN=BK0756795<br><br> <br><br>书目名称Probabilistic Graphical Models网络公开度<br> http://impactfactor.cn/at/?ISSN=BK0756795<br><br> <br><br>书目名称Probabilistic Graphical Models网络公开度学科排名<br> http://impactfactor.cn/atr/?ISSN=BK0756795<br><br> <br><br>书目名称Probabilistic Graphical Models被引频次<br> http://impactfactor.cn/tc/?ISSN=BK0756795<br><br> <br><br>书目名称Probabilistic Graphical Models被引频次学科排名<br> http://impactfactor.cn/tcr/?ISSN=BK0756795<br><br> <br><br>书目名称Probabilistic Graphical Models年度引用<br> http://impactfactor.cn/ii/?ISSN=BK0756795<br><br> <br><br>书目名称Probabilistic Graphical Models年度引用学科排名<br> http://impactfactor.cn/iir/?ISSN=BK0756795<br><br> <br><br>书目名称Probabilistic Graphical Models读者反馈<br> http://impactfactor.cn/5y/?ISSN=BK0756795<br><br> <br><br>书目名称Probabilistic Graphical Models读者反馈学科排名<br> http://impactfactor.cn/5yr/?ISSN=BK0756795<br><br> <br><br>
Individual
发表于 2025-3-21 23:41:59
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失望昨天
发表于 2025-3-22 02:18:11
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
发表于 2025-3-22 06:52:39
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
发表于 2025-3-22 12:46:15
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
图画文字
发表于 2025-3-22 15:39:29
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
发表于 2025-3-22 17:48:54
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
漫不经心
发表于 2025-3-22 23:22:39
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
发表于 2025-3-23 01:49:26
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stratum-corneum
发表于 2025-3-23 06:47:38
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