A Novel Method for Osteometric Reassociation Using Hamiltonian Markov Chain Monte Carlo (MCMC) Simulation

Slice Samoling as a Markov Chain Monte Carlo Method

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Markov Chain Monte Carlo posterior sampling with the Hamiltonian method

The Markov Chain Monte Carlo technique provides a means for drawing random samples from a target probability density function (pdf). MCMC allows one to assess the uncertainties in a Bayesian analysis described by a numerically calculated posterior distribution. This paper describes the Hamiltonian MCMC technique in which a momentum variable is introduced for each parameter of the target pdf. In...

Comparison of two Markov chain Monte Carlo (MCMC) methods

As the world advances, statisticians/mathematicians are being involved into more and more complex surveys for the development of society and human beings. Consequently, these complex survey data requires complicated and high-dimensional models for final analysis. We need sophisticated and efficient statistical/mathematical tools for estimation and prediction of these models. Frequently, we simu...

Markov Chain Monte Carlo Simulation Made Simple

Utilizing Markov Chain Monte Carlo (MCMC) Method for Improved Glottal Inverse Filtering

This paper presents a new glottal inverse filtering (GIF) method that utilizes Markov chain Monte Carlo (MCMC) algorithm. First, initial estimates of the vocal tract and glottal flow are evaluated by an existing GIF method, the iterative adaptive inverse filtering (IAIF). Simultaneously, the initially estimated glottal flow is synthesized using the Klatt model and filtered with the estimated vo...