A simple introduction to Markov Chain Monte–Carlo sampling

A simple introduction to Markov Chain Monte–Carlo sampling

Markov Chain Monte-Carlo (MCMC) is an increasingly popular method for obtaining information about distributions, especially for estimating posterior distributions in Bayesian inference. This article provides a very basic introduction to MCMC sampling. It describes what MCMC is, and what it can be used for, with simple illustrative examples. Highlighted are some of the benefits and limitations o...

Introduction to Markov Chain Monte Carlo

Despite a few notable uses of simulation of random processes in the pre-computer era (Hammersley and Handscomb, 1964, Section 1.2; Stigler, 2002, Chapter 7), practical widespread use of simulation had to await the invention of computers. Almost as soon as computers were invented, they were used for simulation (Hammersley and Handscomb, 1964, Section 1.2). The name “Monte Carlo” started as cuten...

An Introduction to Intuitionistic Markov Chain

A New Markov Chain Based Acceptance Sampling Policy via the Minimum Angle Method

We develop an optimization model based on Markovian approach to determine the optimum value of thresholds in a proposed acceptance sampling design. Consider an acceptance sampling plan where items are inspected and when the number of conforming items between successive defective items falls below a lower control threshold value, then the batch is rejected, and if it falls above a control thresh...

Term Clusters Evaluation by Montecarlo Sampling

Huge amount of textual information available in firms and institutions triggers the need for robust textual data analysis systems. A new field called text-mining has the goal of discovering hidden information and knowledge structuring in texts. Statistical methods coupled with natural language processing can give some answers to this kind of problems. We have developed a module of term clusteri...