On the Hierarchical Bernoulli Mixture Model Using Bayesian Hamiltonian Monte Carlo

被引：0

作者：

Suryaningtyas, Wahyuni ^{[1
,2
]}

Iriawan, Nur ^{[1
]}

Kuswanto, Heri ^{[1
]}

Zain, Ismaini ^{[1
]}

机构：

[1] Inst Teknol Sepuluh Nopember, Fac Sci & Data Analyt, Dept Stat, Surabaya 60111, Indonesia

[2] Univ Muhammadiyah Surabaya, Study Program Math Educ, Fac Teacher Training & Educ, Jl Sutorejo 59, Surabaya 60113, Indonesia

来源：

SYMMETRY-BASEL | 2021年 / 13卷 / 12期

关键词：

Bernoulli mixture model; finite mixture; Hamiltonian Monte Carlo; WAIC; MULTILEVEL MODELS;

D O I：

10.3390/sym13122404

中图分类号：

O [数理科学和化学]; P [天文学、地球科学]; Q [生物科学]; N [自然科学总论];

学科分类号：

07 ; 0710 ; 09 ;

摘要：

The model developed considers the uniqueness of a data-driven binary response (indicated by 0 and 1) identified as having a Bernoulli distribution with finite mixture components. In social science applications, Bernoulli's constructs a hierarchical structure data. This study introduces the Hierarchical Bernoulli mixture model (Hibermimo), a new analytical model that combines the Bernoulli mixture with hierarchical structure data. The proposed approach uses a Hamiltonian Monte Carlo algorithm with a No-U-Turn Sampler (HMC/NUTS). The study has performed a compatible syntax program computation utilizing the HMC/NUTS to analyze the Bayesian Bernoulli mixture aggregate regression model (BBMARM) and Hibermimo. In the model estimation, Hibermimo yielded a result of ~90% compliance with the modeling of each district and a small Widely Applicable Information Criteria (WAIC) value.

引用

页数：20

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