Applying Reversible Jump MCMC to Detect Cancer Associated Genes

2018 ◽  
Author(s):  
Bo Yang ◽  
Zheng Wu
Keyword(s):  
Reversible Jump ◽  
Reversible Jump Mcmc

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.

Robust Full Bayesian Learning for Radial Basis Networks

2001 ◽  
Vol 13 (10) ◽  
pp. 2359-2407 ◽  
Author(s):  
Christophe Andrieu ◽  
Nando de Freitas ◽  
Arnaud Doucet
Keyword(s):  
Simulated Annealing ◽  
Convergence Theorem ◽  
Bayesian Learning ◽  
Simulated Annealing Algorithm ◽  
Minimum Description Length ◽  
Information Criterion ◽  
Model Parameters ◽  
Reversible Jump ◽  
Reversible Jump Mcmc ◽  
Radial Basis

We propose a hierarchical full Bayesian model for radial basis networks. This model treats the model dimension (number of neurons), model parameters, regularization parameters, and noise parameters as unknown random variables. We develop a reversible-jump Markov chain Monte Carlo (MCMC) method to perform the Bayesian computation. We find that the results obtained using this method are not only better than the ones reported previously, but also appear to be robust with respect to the prior specification. In addition, we propose a novel and computationally efficient reversible-jump MCMC simulated annealing algorithm to optimize neural networks. This algorithm enables us to maximize the joint posterior distribution of the network parameters and the number of basis function. It performs a global search in the joint space of the parameters and number of parameters, thereby surmounting the problem of local minima to a large extent. We show that by calibrating the full hierarchical Bayesian prior, we can obtain the classical Akaike information criterion, Bayesian information criterion, and minimum description length model selection criteria within a penalized likelihood framework. Finally, we present a geometric convergence theorem for the algorithm with homogeneous transition kernel and a convergence theorem for the reversible-jump MCMC simulated annealing method.

A Bayesian Lasso via reversible-jump MCMC

2011 ◽  
Vol 91 (8) ◽  
pp. 1920-1932 ◽  
Author(s):  
Xiaohui Chen ◽  
Z. Jane Wang ◽  
Martin J. McKeown
Keyword(s):  
Reversible Jump ◽  
Reversible Jump Mcmc ◽  
Bayesian Lasso
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