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→贝叶斯网络 Bayesian networks
==== 贝叶斯网络 Bayesian networks ====
==== 贝叶斯网络 Bayesian networks ====
[[Image:SimpleBayesNetNodes.svg|thumb|right|A simple Bayesian network. Rain influences whether the sprinkler is activated, and both rain and the sprinkler influence whether the grass is wet. 一个简单的贝叶斯网路。雨水会影响喷头是否被激活，雨水和喷头都会影响草地是否湿润。]]
[[Image:SimpleBayesNetNodes.svg|thumb|right|A simple Bayesian network. Rain influences whether the sprinkler is activated, and both rain and the sprinkler influence whether the grass is wet. 一个简单的贝叶斯网路。雨水会影响喷头是否被激活，而雨水和喷头都会影响草地是否湿润。]]
:''主文章：[https://en.wikipedia.org/wiki/Bayesian_network 贝叶斯网络]''
'''贝叶斯网路 Bayesian Network'''，或称信任网络或者有向无环图形模型是通过'''[https://en.wikipedia.org/wiki/Directed_acyclic_graph 有向无环图] DAG'''表示一组[https://en.wikipedia.org/wiki/Random_variable 随机变量]及其[https://en.wikipedia.org/wiki/Conditional_independence 条件独立性]的[https://en.wikipedia.org/wiki/Graphical_model 概率图形模型]。例如，贝叶斯网络可以表示疾病和症状之间的概率关系。给定症状，网络可以用来计算各种疾病出现的概率。有效的算法可以进行[https://en.wikipedia.org/wiki/Inference 推理]和学习。
现有的高效算法可以执行推理和学习。贝叶斯网络模型的变量序列，如语音信号或蛋白质序列，被称为动态贝叶斯网络。而贝叶斯网络能够表示和解决不确定性决策问题的推广称为影响图。
A Bayesian network, belief network or directed acyclic graphical model is a probabilistic graphical model that represents a set of random variables and their conditional independence with 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. Efficient algorithms exist that perform inference and learning. Bayesian networks that model sequences of variables, like speech signals or protein sequences, are called dynamic Bayesian networks. Generalizations of Bayesian networks that can represent and solve decision problems under uncertainty are called influence diagrams.
A Bayesian network, belief network or directed acyclic graphical model is a probabilistic graphical model that represents a set of random variables and their conditional independence with 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. Efficient algorithms exist that perform inference and learning. Bayesian networks that model sequences of variables, like speech signals or protein sequences, are called dynamic Bayesian networks. Generalizations of Bayesian networks that can represent and solve decision problems under uncertainty are called influence diagrams.
==== 遗传算法 Genetic algorithms ====
==== 遗传算法 Genetic algorithms ====