The Exact Inference of Beta Process and Beta Bernoulli Process From Finite Observations
作者机构：School of Artificial Intelligence and AutomationHuazhong University of Science and TechnologyWuhan430074China School of Computer Science and TechnologyHuazhong University of Science and TechnologyWuhan430074China.
出 版 物：《Computer Modeling in Engineering & Sciences》 (工程与科学中的计算机建模（英文）)
年 卷 期：2019年第121卷第10期
页 面：49-82页
核心收录：
学科分类：07[理学] 0701[理学-数学] 070101[理学-基础数学]
主 题：Beta process joint distribution beta Bernoulli process exact inference
摘 要：Beta Process is a typical nonparametric Bayesian *** the Beta Bernoulli Process provides a Bayesian nonparametric prior for models involving collections of binary valued *** previous studies considered the Beta Process inference problem by giving the Stick-Breaking sampling *** paper focuses on analyzing the form of precise probability distribution based on a Stick-Breaking approach,that is,the joint probability distribution is derived from any finite number of observable samples:It not only determines the probability distribution function of the Beta Process with finite observation(represented as a group of number between[0,1]),but also gives the distribution function of the Beta Bernoulli Process with the same finite dimension(represented as a matrix with element value of 0 or,1)by using this distribution as a prior.
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