Reversible jump MCMC for joint detection and estimation of sources in colored noise

2002 ◽  
Vol 50 (2) ◽  
pp. 231-240 ◽  
Author(s):  
J.-R. Larocque ◽  
J.P. Reilly
Keyword(s):  
Colored Noise ◽  
Reversible Jump ◽  
Joint Detection ◽  
Reversible Jump Mcmc ◽  
Detection And Estimation

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 Bayesian sequential joint detection and estimation

2018 ◽  
Vol 37 (4) ◽  
pp. 530-570 ◽  
Author(s):  
Dominik Reinhard ◽  
Michael Fauß ◽  
Abdelhak M. Zoubir
Keyword(s):  
Joint Detection ◽  
Detection And Estimation
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