Mathematics and Statistics Vol. 8(2A), pp. 17 - 22
DOI: 10.13189/ms.2020.081303
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Bayesian Estimation in Piecewise Constant Model with Gamma Noise by Using Reversible Jump MCMC

Suparman ^*
Department of Mathematics Education, University of Ahmad Dahlan, Indonesia

ABSTRACT

A piecewise constant model is often applied to model data in many fields. Several noises can be added in the piecewise constant model. This paper proposes the piecewise constant model with a gamma multiplicative noise and a method to estimate a parameter of the model. The estimation is done in a Bayesian framework. A prior distribution for the model parameter is chosen. The prior distribution for the parameter model is multiplied with a likelihood function for the data to build a posterior distribution for the parameter. Because a number of models are also parameters, a form of the posterior distribution for the parameter is too complex. A Bayes estimator cannot be calculated easily. A reversible jump Monte Carlo Markov Chain (MCMC) is used to find the Bayes estimator of the model parameter. A result of this paper is the development of the piecewise constant model and the method to estimate the model parameter. An advantage of this method can simultaneously estimate the constant piecewise model parameter.

KEYWORDS
Bayesian, Gamma Noise, Piecewise Constant, Reversible Jump MCMC

Cite This Paper in IEEE or APA Citation Styles
(a). IEEE Format:
[1] Suparman , "Bayesian Estimation in Piecewise Constant Model with Gamma Noise by Using Reversible Jump MCMC," Mathematics and Statistics, Vol. 8, No. 2A, pp. 17 - 22, 2020. DOI: 10.13189/ms.2020.081303.

(b). APA Format:
Suparman (2020). Bayesian Estimation in Piecewise Constant Model with Gamma Noise by Using Reversible Jump MCMC. Mathematics and Statistics, 8(2A), 17 - 22. DOI: 10.13189/ms.2020.081303.