Estimasi Parameter Model Poisson Hidden Markov Pada Data Banyaknya Kedatangan Klaim Asuransi Jiwa

Abstract

<jats:p>The Poisson hidden Markov model is a model that consists of two parts. The first part is the cause of events that are hidden or cannot be observed directly and form a Markov chain, while the second part is the process of observation or observable parts that depend on the cause of the event and following the Poisson distribution. The Poisson hidden Markov model parameters are estimated using the Maximum Likelihood Estimator (MLE). But it is difficult to find analytical solutions from the ln-likelihood function. Therefore, the Expectation Maximization (EM) algorithm is used to obtain its numerical solutions which are then applied to life insurance data. The best model is obtained with 2 states or m = 2 based on the smallest Bayesian Information Criterion (BIC) value of 338,778 and the average predicted number of claims arrivals is 0.385 per day.</jats:p>

Modeling the Many Earthquakes in Sumatra Using Poisson Hidden Markov Models and Expectation Maximization Algorithm

“…The hidden Markov model is a discrete-time model consisting of two parts. The first part is the hidden or unobservable cause of the event and forms a Markov chain, while the second part is the observation process or the observed part, which depends on the event's cause [9].…”