A Bayesian Approach for Zero-Inflated Count Regression Models by Using the Reversible Jump Markov Chain Monte Carlo Method and an Application

2010 ◽  
Vol 39 (12) ◽  
pp. 2109-2127 ◽  
Author(s):  
İlknur Özmen ◽  
Haydar Demirhan
Keyword(s):  
Monte Carlo ◽  
Markov Chain ◽  
Markov Chain Monte Carlo ◽  
Monte Carlo Method ◽  
Bayesian Approach ◽  
Regression Models ◽  
Reversible Jump

Bayesian Approach to Study of Unfolded Bifurcation Structure via Markov Chain Monte Carlo Method

2017 ◽  
Vol 14 (3) ◽  
pp. 1661-1666
Author(s):  
EssamO. Abdel-Rahman ◽  
Mahmoud Elmezain ◽  
Zohair.S.A. Malki ◽  
GamalA. Abd-Elmougod
Keyword(s):  
Monte Carlo ◽  
Markov Chain ◽  
Markov Chain Monte Carlo ◽  
Monte Carlo Method ◽  
Bayesian Approach ◽  
Bifurcation Structure

scholarly journals Automated Graduation using Bayesian Trans-dimensional Models

2011 ◽  
Vol 5 (2) ◽  
pp. 231-251 ◽  
Author(s):  
R.J. Verrall ◽  
S. Haberman
Keyword(s):  
Monte Carlo ◽  
Markov Chain ◽  
Markov Chain Monte Carlo ◽  
Bayesian Approach ◽  
Model Uncertainty ◽  
Weighted Average ◽  
New Method ◽  
Reversible Jump ◽  
Standard Methods ◽  
Wide Range

AbstractThis paper presents a new method of graduation which uses parametric formulae together with Bayesian reversible jump Markov chain Monte Carlo methods. The aim is to provide a method which can be applied to a wide range of data, and which does not require a lot of adjustment or modification. The method also does not require one particular parametric formula to be selected: instead, the graduated values are a weighted average of the values from a range of formulae. In this way, the new method can be seen as an automatic graduation method which we believe can be applied in many cases without any adjustments and provide satisfactory graduated values. An advantage of a Bayesian approach is that it allows for model uncertainty unlike standard methods of graduation.

Resolution of Closely Spaced Objects via Infrared Focal Plane Using Reversible Jump Markov Chain Monte-Carlo Method

2011 ◽  
Vol 31 (5) ◽  
pp. 0510004 ◽  
Author(s):  
林两魁 Lin Liangkui ◽  
徐晖 Xu Hui ◽  
许丹 Xu Dan ◽  
安玮 An Wei
Keyword(s):  
Monte Carlo ◽  
Markov Chain ◽  
Markov Chain Monte Carlo ◽  
Monte Carlo Method ◽  
Focal Plane ◽  
Reversible Jump
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