BibTex Citation Data :
@article{Medstat2503, author = {Moch. Mukid and Sugito Sugito}, title = {IMPLEMENTASI MARKOV CHAIN MONTE CARLO PADA PENDUGAAN HYPERPARAMETER REGRESI PROSES GAUSSIAN}, journal = {MEDIA STATISTIKA}, volume = {4}, number = {1}, year = {2011}, keywords = {}, abstract = { This paper studies the implementation of Markov Chain Monte Carlo on estimating the hyperparameter of Gaussian process. Metropolish-Hasting (MH) algorithm is used to generate the random samples from the posterior distribution that can not be generated by a direct simulation method. This algorithm require only a proposal distribution for generating a candidate point. In this paper uniform distribution is choosen as the proposal distribution. Keywords : Markov Chain Monte Carlo, Gaussian Process, Metropolis-Hasting Algorithm }, issn = {2477-0647}, pages = {1--10} doi = {10.14710/medstat.4.1.1-10}, url = {https://ejournal.undip.ac.id/index.php/media_statistika/article/view/2503} }
Refworks Citation Data :
This paper studies the implementation of Markov Chain Monte Carlo on estimating the hyperparameter of Gaussian process. Metropolish-Hasting (MH) algorithm is used to generate the random samples from the posterior distribution that can not be generated by a direct simulation method. This algorithm require only a proposal distribution for generating a candidate point. In this paper uniform distribution is choosen as the proposal distribution.
Keywords: Markov Chain Monte Carlo, Gaussian Process, Metropolis-Hasting Algorithm
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