@蓝蓝小脚丫 @好东西传送门 有讲贝叶斯网络拓扑结构的么，BBN结构和CPT

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http://www.weibo.com/3774704830/BrpAC2mPE

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概念

http://en.wikipedia.org/wiki/Bayesian_network A Bayesian network, Bayes network, belief network, Bayes(ian) model or probabilistic directed acyclic graphical model is a probabilistic graphical model (a type of statistical model) that represents a set of random variables and their conditional dependencies via a directed acyclic graph (DAG). For example, a Bayesian network could represent the probabilistic relationships between diseases and symptoms. Given symptoms, the network can be used to compute the probabilities of the presence of various diseases.

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http://en.wikipedia.org/wiki/Conditional_probability_table In statistics, the conditional probability table (CPT) is defined for a set of discrete (not independent) random variables to demonstrate marginal probability of a single variable with respect to the others.

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http://en.wikipedia.org/wiki/Bayesian_network#Structure_learning

Automatically learning the graph structure of a Bayesian network is a challenge pursued within machine learning. The basic idea goes back to a recovery algorithm developed by Rebane and Pearl (1987)[6] and rests on the distinction between the three possible types of adjacent triplets allowed in a directed acyclic graph (DAG): X \rightarrow Y \rightarrow Z X \leftarrow Y \rightarrow Z X \rightarrow Y \leftarrow Z

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https://github.com/memect/hao/blob/95bd1b6d2b44ebc30f3337d901e20ef3b183b273/awesome/bayesian-network-python.md Bayesian network 与python概率编程实战入门与进阶

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Automatically learning the graph structure of a Bayesian network is a challenge pursued within machine learning. The basic idea goes back to a recovery algorithm developed by Rebane and Pearl (1987)[6] and rests on the distinction between the three possible types of adjacent triplets allowed in a directed acyclic graph (DAG): X \rightarrow Y \rightarrow Z X \leftarrow Y \rightarrow Z X \rightarrow Y \leftarrow Z