TY  - EJOU
AU  - Cheng, Yang 
AU  - Li, Dehua 
AU  - Jiang, Wenbin 

TI  - The Exact Inference of Beta Process and Beta Bernoulli Process From Finite Observations
T2  - Computer Modeling in Engineering \& Sciences

PY  - 2019
VL  - 121
IS  - 1
SN  - 1526-1506

AB  - Beta Process is a typical nonparametric Bayesian model. and the Beta Bernoulli Process provides a Bayesian nonparametric prior for models involving collections of binary valued features. Some previous studies considered the Beta Process inference problem by giving the Stick-Breaking sampling method. This 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.
KW  - Beta process
KW  -  joint distribution
KW  -  beta Bernoulli process
KW  -  exact inference

DO  - 10.32604/cmes.2019.07657