Abstrak 
Bayesian Approach On Parameter Estimation In Hidden Markov Model
Dwi Agustin N.S, Septiadi Padmadisastra, Sudartianto
Universitas Padjadjaran, ARPN Journal of Engineering and Applied Sciences Vol. 9, No. 9, September 2014 ISSN 1819-6608, www.arpnjounials.com
Bahasa Inggris
Universitas Padjadjaran, ARPN Journal of Engineering and Applied Sciences Vol. 9, No. 9, September 2014 ISSN 1819-6608, www.arpnjounials.com
Bayesian, Gibbs Sampler, Hidden Markov models, Schmidth and Fergusson's Climate Classification
This paper presents study about the parameter estimation in hidden markov model. The approach is taken from a Bayesian method, there will be two sources of information,there are information from the likelihood function and the prior function. This approach will be applied to daily rainfall data in Darajat, Garut. The numbers of hidden states are used in this paper based on Schmidth and Fergusson’s climate classification which are suitable to the local conditions. This classification was obtained three types of division in the period of one year where the condition called wet months when monthly rainfall > 100 mm per month, moist months whet monthly rainfall between 100 – 60 mm and the dry months when monthly rainfall <60 mm per month. The process estimation of hidden markov parameters is using Gibbs Sampler algorithm.