PEMBANGKITAN SAMPEL RANDOM MENGGUNAKAN ALGORITMA METROPOLIS-HASTINGS

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Section: Articles
Language: EN
Statistics: 149 110
Abstract
Generating random samples can be done directly and indirectly using simulation techniques. This final project will discuss the process of generating random samples and estimate the parameters using an indirect simulation. Indirect simulation techniques used if the target distribution has a complicated shape and high dimension of density functions. Markov Chain Monte Carlo (MCMC) simulation is a solution to do it. One of the algorithms that is commonly used is Metropolis-Hastings. This algorithm uses the mechanism of acceptance and rejection to generate a sequence of random samples. In the example to be discussed, Metropolis-Hastings algorithm is applied to generate random samples of Beta distribution and also estimate the parameter value of the Poisson distribution using a proposal distribution random-walk Metropolis.
Keywords: Markov Chain Monte Carlo, Metropolis-Hastings algorithm, proposal distribution

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