A fully Bayesian model based on reversible jump MCMC and finite Beta mixtures for clustering

2012 ◽  
Vol 39 (5) ◽  
pp. 5946-5959 ◽  
Author(s):  
Nizar Bouguila ◽  
Tarek Elguebaly
Keyword(s):  
Bayesian Model ◽  
Reversible Jump ◽  
Reversible Jump Mcmc ◽  
Model Based ◽  
Fully Bayesian

Adaptive Proposal Construction for Reversible Jump MCMC

2008 ◽  
Vol 35 (4) ◽  
pp. 677-690 ◽  
Author(s):  
RICARDO S. EHLERS ◽  
STEPHEN P. BROOKS
Keyword(s):  
Reversible Jump ◽  
Reversible Jump Mcmc

Automating and evaluating reversible jump MCMC proposal distributions

2008 ◽  
Vol 19 (4) ◽  
pp. 409-421 ◽  
Author(s):  
Y. Fan ◽  
G. W. Peters ◽  
S. A. Sisson
Keyword(s):  
Reversible Jump ◽  
Reversible Jump Mcmc

scholarly journals Forecasting Software Using Laplacian AR Model based on Bootstrap-Reversible Jump MCMC: Application on Stock Price Data

Webology ◽  
2021 ◽  
Vol 18 (Special Issue 04) ◽  
pp. 1045-1055
Author(s):  
Sup arman ◽  
Yahya Hairun ◽  
Idrus Alhaddad ◽  
Tedy Machmud ◽  
Hery Suharna ◽  
...  
Keyword(s):  
Stock Price ◽  
Complex Structure ◽  
Arma Model ◽  
Bayesian Estimator ◽  
Ar Model ◽  
Reversible Jump ◽  
Mcmc Algorithm ◽  
Reversible Jump Mcmc ◽  
Variable Dimension ◽  
Fixed Dimension

The application of the Bootstrap-Metropolis-Hastings algorithm is limited to fixed dimension models. In various fields, data often has a variable dimension model. The Laplacian autoregressive (AR) model includes a variable dimension model so that the Bootstrap-Metropolis-Hasting algorithm cannot be applied. This article aims to develop a Bootstrap reversible jump Markov Chain Monte Carlo (MCMC) algorithm to estimate the Laplacian AR model. The parameters of the Laplacian AR model were estimated using a Bayesian approach. The posterior distribution has a complex structure so that the Bayesian estimator cannot be calculated analytically. The Bootstrap-reversible jump MCMC algorithm was applied to calculate the Bayes estimator. This study provides a procedure for estimating the parameters of the Laplacian AR model. Algorithm performance was tested using simulation studies. Furthermore, the algorithm is applied to the finance sector to predict stock price on the stock market. In general, this study can be useful for decision makers in predicting future events. The novelty of this study is the triangulation between the bootstrap algorithm and the reversible jump MCMC algorithm. The Bootstrap-reversible jump MCMC algorithm is useful especially when the data is large and the data has a variable dimension model. The study can be extended to the Laplacian Autoregressive Moving Average (ARMA) model.

scholarly journals Hybrid elicitation and indirect Bayesian inference with quantile-parametrized likelihood

2021 ◽  
Author(s):  
Dmytro Perepolkin ◽  
Benjamin Goodrich ◽  
Ullrika Sahlin
Keyword(s):  
Bayesian Inference ◽  
Bayesian Model ◽  
Probability Distributions ◽  
Expert Elicitation ◽  
Model Based ◽  
Dirichlet Prior

This paper extends the application of indirect Bayesian inference to probability distributions defined in terms of quantiles of the observable quantities. Quantile-parameterized distributions are characterized by high shape flexibility and interpretability of its parameters, and are therefore useful for elicitation on observables. To encode uncertainty in the quantiles elicited from experts, we propose a Bayesian model based on the metalog distribution and a version of the Dirichlet prior. The resulting “hybrid” expert elicitation protocol for characterizing uncertainty in parameters using questions about the observable quantities is discussed and contrasted to parametric and predictive elicitation.

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