Bayesian inference for skew-wrapped Cauchy mixture model using a modified Gibbs sampler

2021 ◽  
Author(s):  
Najmeh Nakhaei Rad ◽  
Andriette Bekker ◽  
Mohammad Arashi
Keyword(s):  
Bayesian Inference ◽  
Mixture Model ◽  
Gibbs Sampler

scholarly journals A Gibbs Sampler for a Hierarchical Dirichlet Process Mixture Model

2019 ◽  
Author(s):  
Mark Andrews
Keyword(s):  
Mixture Model ◽  
Gibbs Sampler ◽  
Dirichlet Process ◽  
Dirichlet Process Mixture ◽  
Hierarchical Dirichlet Process ◽  
Dirichlet Process Mixture Model ◽  
Multinomial Data

A Gibbs sampler for the hierarchical Dirichlet process mixture model (HDPMM) when used with multinomial data.

Modeling the crashes count using finite mixture models

2019 ◽  
Vol 7 (1) ◽  
pp. 13-27
Author(s):  
Safaa K. Kadhem ◽  
Sadeq A. Kadhim
Keyword(s):  
Bayesian Inference ◽  
Mixture Models ◽  
Gibbs Sampler ◽  
Simulation Study ◽  
Finite Mixture Models ◽  
Synthetic Data ◽  
Finite Mixture Model ◽  
Finite Mixture ◽  
Model Parameters ◽  
Mcmc Method

"This paper aims at the modeling the crashes count in Al Muthanna governance using finite mixture model. We use one of the most common MCMC method which is called the Gibbs sampler to implement the Bayesian inference for estimating the model parameters. We perform a simulation study, based on synthetic data, to check the ability of the sampler to find the best estimates of the model. We use the two well-known criteria, which are the AIC and BIC, to determine the best model fitted to the data. Finally, we apply our sampler to model the crashes count in Al Muthanna governance.

scholarly journals Bayesian Analysis of Mixtures of Factor Analyzers

2001 ◽  
Vol 13 (5) ◽  
pp. 993-1002 ◽  
Author(s):  
Akio Utsugi ◽  
Toru Kumagai
Keyword(s):  
Bayesian Inference ◽  
Bayesian Analysis ◽  
Gibbs Sampler ◽  
Simulation Experiment ◽  
Estimation Algorithm ◽  
Deterministic Algorithm ◽  
Maximum A Posteriori ◽  
A Posteriori ◽  
Conjugate Priors ◽  
Posteriori Estimation

For Bayesian inference on the mixture of factor analyzers, natural conjugate priors on the parameters are introduced, and then a Gibbs sampler that generates parameter samples following the posterior is constructed. In addition, a deterministic estimation algorithm is derived by taking modes instead of samples from the conditional posteriors used in the Gibbs sampler. This is regarded as a maximum a posteriori estimation algorithm with hyperparameter search. The behaviors of the Gibbs sampler and the deterministic algorithm are compared on a simulation experiment.

Bayesian inference on GARCH models using the Gibbs sampler

1998 ◽  
Vol 1 (1) ◽  
pp. C23-C46 ◽  
Author(s):  
Luc Bauwens ◽  
Michel Lubrano
Keyword(s):  
Bayesian Inference ◽  
Gibbs Sampler ◽  
Garch Models
Sign in / Sign up

Export Citation Format

Share Document