Detection and estimation of general frequency-modulated signals using reversible jump MCMC methods

1999 ◽  
Author(s):  
Keith D. Copsey ◽  
Neil J. Gordon ◽  
Alan Marrs
Keyword(s):  
Mcmc Methods ◽  
Reversible Jump ◽  
Reversible Jump Mcmc ◽  
Detection And Estimation ◽  
Modulated Signals ◽  
General Frequency

Reversible jump MCMC methods for fully automatic motion analysis in tagged MRI

2012 ◽  
Vol 16 (1) ◽  
pp. 301-324 ◽  
Author(s):  
Ihor Smal ◽  
Noemí Carranza-Herrezuelo ◽  
Stefan Klein ◽  
Piotr Wielopolski ◽  
Adriaan Moelker ◽  
...  
Keyword(s):  
Motion Analysis ◽  
Mcmc Methods ◽  
Reversible Jump ◽  
Tagged Mri ◽  
Reversible Jump Mcmc ◽  
Fully Automatic

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.

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