Abstract

In the Bayesian mixture modeling requires stages the identification number of the most appropriate mixture components thus obtained mixture models fit the data through data driven concept. Reversible Jump Markov Chain Monte Carlo (RJMCMC) is a combination of the reversible jump (RJ) concept and the Markov Chain Monte Carlo (MCMC) concept used by some researchers to solve the problem of identifying the number of mixture components which are not known with certainty number. In its application, RJMCMC using the concept of the birth/death and the split-merge with six types of movement, that are w updating, θ updating, z updating, hyperparameter β updating, split-merge for components and birth/death from blank components. The development of the RJMCMC algorithm needs to be done according to the observed case. The purpose of this study is to know the performance of RJMCMC algorithm development in identifying the number of mixture components which are not known with certainty number in the Bayesian mixture modeling for microarray data in Indonesia. The results of this study represent that the concept RJMCMC algorithm development able to properly identify the number of mixture components in the Bayesian normal mixture model wherein the component mixture in the case of microarray data in Indonesia is not known for certain number.

Original languageEnglish
Title of host publicationInternational Conference and Workshop on Mathematical Analysis and its Applications, ICWOMAA 2017
EditorsAdem Kilicman, Marjono, Ratno Bagus Edy Wibowo, Moch. Aruman Imron
PublisherAmerican Institute of Physics Inc.
ISBN (Electronic)9780735416055
DOIs
Publication statusPublished - 5 Dec 2017
EventInternational Conference and Workshop on Mathematical Analysis and its Applications, ICWOMAA 2017 - Malang, Indonesia
Duration: 2 Aug 20173 Aug 2017

Publication series

NameAIP Conference Proceedings
Volume1913
ISSN (Print)0094-243X
ISSN (Electronic)1551-7616

Conference

ConferenceInternational Conference and Workshop on Mathematical Analysis and its Applications, ICWOMAA 2017
Country/TerritoryIndonesia
CityMalang
Period2/08/173/08/17

Keywords

  • Bayesian Mixture
  • Microarray-Indonesia
  • RJMCMC Algorithm Development

Fingerprint

Dive into the research topics of 'Development of reversible jump Markov Chain Monte Carlo algorithm in the Bayesian mixture modeling for microarray data in Indonesia'. Together they form a unique fingerprint.

Cite this