Suparman, Dr. (2014) METODE REVERSIBLE JUMP MARKOV CHAIN MONTE CARLO: Estimasi Bayesian dalam Model Regresi Linear per Potongan. In: Senari, 1 November 2014, Bali.
Preview |
Text
141501_B1_16_Senari.pdf - Accepted Version Download (31MB) | Preview |
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: | Q Science > QA Mathematics > QA75 Electronic computers. Computer science |
| Divisi / Prodi: | Faculty of Teacher Training and Education (Fakultas Keguruan dan Ilmu Pendidikan) > S1-Mathematics Education (S1-Pendidikan Matematika) |
| Depositing User: | Dr. Suparman M, Si., DEA |
| Date Deposited: | 16 Jun 2016 15:16 |
| Last Modified: | 16 Jun 2016 15:16 |
| URI: | http://eprints.uad.ac.id/id/eprint/3056 |
Actions (login required)
 |
View Item |