Inference and sampling of gamma poisson process stick breaking construction model

Yang Cheng; Dehua Li

doi:10.1117/12.2557457

14 February 2020 Inference and sampling of gamma poisson process stick breaking construction model

Yang Cheng, Dehua Li

Proceedings Volume 11430, MIPPR 2019: Pattern Recognition and Computer Vision; 1143025 (2020) https://doi.org/10.1117/12.2557457
Event: Eleventh International Symposium on Multispectral Image Processing and Pattern Recognition (MIPPR2019), 2019, Wuhan, China

Abstract

At present, there are three main implementations of Nonparametrics Bayes model in machine learning: Dirichlet Process and CRP model, Beta Process and Beta Bernouilli Process model, Gamma Process and Gamma Poisson Process model. Aiming at the infinite sampling process constructed by Gamma Process Stick Breaking proposed by Anirban Roychowdhury, this paper discusses the problem of exact inference based on a finite number of observation samples, analyzes the exact probability distribution function of Gamma Process Stick Breaking construction, and takes this distribution function as a priori, and applies the corresponding results to the inference of Gamma Poisson process.

Citation Download Citation

Yang Cheng and Dehua Li "Inference and sampling of gamma poisson process stick breaking construction model", Proc. SPIE 11430, MIPPR 2019: Pattern Recognition and Computer Vision, 1143025 (14 February 2020); https://doi.org/10.1117/12.2557457

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