PBIL algorithm is a probability learning based evolutionary algorithm.
PBIL算法是一种基于概率分析的进化算法。
Probability theory captures a number of essential characteristics of human cognition, including aspects of perception, reasoning, belief revision, and learning.
机率理论涵盖了人类认知中的许多重要特征，包括感知、推理、信念改变和学习方面。
The characteristic of the Bayes method is to use probability to express the uncertainty of all forms, learning and the reasoning of other forms are all realized with the rule of probability.
贝叶斯方法的特点是使用概率去表示所有形式的不确定性，学习或其他形式的推理都用概率规则来实现。
Probability theory is the only reasonable way to represent uncertainty, it is useful for machine learning or reasoning under uncertainty.
概率论是表示不确定性的唯一合理的方法，概率论对于机器学习或不确定情况下的推理是有用的。
Bayesian learning is a probability method that makes optimal decision based on known probability distribution and recently observed data.
贝叶斯学习是一种基于已知的概率分布和观察到的数据进行推理，做出最优决策的概率手段。
Bayesian learning Theory represents uncertainty with probability and learning and inference are realized by probabilistic rules.
贝叶斯学习理论使用概率去表示所有形式的不确定性，通过概率规则来实现学习和推理过程。
Bayesian learning Theory represents uncertainty with probability and learning and inference are realized by probabilistic rules.
贝叶斯学习理论使用概率去表示所有形式的不确定性，通过概率规则来实现学习和推理过程。
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