Repository UAD

METODE REVERSIBLE JUMP MARKOV CHAIN MONTE CARLO: Estimasi Bayesian dalam Model Regresi Linear per Potongan

Dr., Suparman (2014) METODE REVERSIBLE JUMP MARKOV CHAIN MONTE CARLO: Estimasi Bayesian dalam Model Regresi Linear per Potongan. In: Senari, November 2014, Bali.

[img] PDF
141501_B1_16_Senari.pdf

Download (31MB)

Abstract

The method used to estimate the parameters of piecewise linear regression is Bayesian method. But the Bayes estimator can not be found analytically. To overcome these problems are proposed the Reversible Jump MCMC Algorithm. Reversible Jump MCMC Algorithm generates the Markov chain converges to the limit distribution of the posterior distribution of the parameters of piecewise linear regression models.Bayes estimator for the parameters of piecewise linear regression models obtained by the Markov chain.

Item Type: Conference or Workshop Item (Paper)
Subjects: H Social Sciences > HA Statistics
Depositing User: Dr. Suparman M, Si., DEA
Date Deposited: 01 Oct 2015 00:24
Last Modified: 01 Oct 2015 00:24
URI: http://eprints.uad.ac.id/id/eprint/2433

Actions (login required)

View Item View Item

Repository UAD is powered by EPrints 3 which is developed by the School of Electronics and Computer Science at the University of Southampton. More information and software credits.