This article discusses the parameter estimation for dynamic system by a Bayesian approach
associated with Markov Chain Monte Carlo methods (MCMC). The MCMC methods are …

This article discusses the parameter estimation for dynamic system by a Bayesian approach
associated with Markov Chain Monte Carlo methods (MCMC). The MCMC methods are …

In this paper, we will discuss the performance of Bayesian computational approaches for
estimating the parameters of a Logistic Regression model. Markov Chain Monte Carlo …

Bayesian methods have become increasingly popular in modern statistical analysis and are
being applied to a broad spectrum of scientific fields and research areas. This paper …

Applying Bayesian models became practical with the development of Markov Chain Monte
Carlo (MCMC) methods combined with the advent of increased computing power beginning …

We have explored likelihood functions, iterative methods, and the Metropolis-Hastings
algorithm. In this chapter all these together introduce a sophisticated parameter estimation …

The most common approach currently used in the estimation of Bayesian models is Markov
chain Monte Carlo (MCMC). PyMCMC is a Python module that is designed to simplify the …

The study of parameter estimation of a specified model has a long history. In statistics,
Bayesian analysis via Markov chain Monte Carlo (MCMC) sampling is an efficient way for …

Abstract Markov Chain Monte Carlo is an innovative and widely used computational
methodology for an accurate estimation of a distribution, whose direct numerical evaluation …

Bayesian inference plays an essential role in the development of mathematical theory, as an
important component of statistical methods. The prior distribution, the likelihood function and …