Group metropolis sampling

Abstract

Abstract-Monte Carlo (MC) methods are widely used for Bayesian inference and optimization in statistics, signal processing and machine learning. Two well-known class of MC methods are the Importance Sampling (IS) techniques and the Markov Chain Monte Carlo (MCMC) algorithms. In this work, we introduce the Group Importance Sampling (GIS) framework where different sets of weighted samples are properly summarized with one summary particle and one summary weight. GIS facilitates the design of novel efficient MC techniques. For instance, we present the Group Metropolis Sampling (GMS) algorithm which produces a Markov chain of sets of weighted samples. GMS in general outperforms other multiple try schemes as shown by means of numerical simulations.

Pemodelan Rantai Markov Menggunakan Algoritma Metropolis-Hastings

“…Beberapa kasus menarik, tergantung pada pemilihan density proposal [5]. Ada beberapa cara pemilihan proposal:…”

“…Beberapa perbaikan dan perluasan terbaru diusulkan dalam literatur juga dibahas secara singkat, memberikan gambaran yang cepat tapi lengkap tentang arus pengambilan sampel nyata berbasis Metropolis [5].…”