@Article{cmes.2019.07657,
AUTHOR = {Yang Cheng, Dehua Li, Wenbin Jiang},
TITLE = {The Exact Inference of Beta Process and Beta Bernoulli Process From Finite Observations},
JOURNAL = {Computer Modeling in Engineering \& Sciences},
VOLUME = {121},
YEAR = {2019},
NUMBER = {1},
PAGES = {49--82},
URL = {http://www.techscience.com/CMES/v121n1/34030},
ISSN = {1526-1506},
ABSTRACT = {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.},
DOI = {10.32604/cmes.2019.07657}
}