Displaying similar documents to “A Bayesian model for binary Markov chains.”

Estimating the shape parameter of the Topp-Leone distribution based on Type I censored samples

Husam Awni Bayoud (2015)

Applicationes Mathematicae

Similarity:

The shape parameter of the Topp-Leone distribution is estimated from classical and Bayesian points of view based on Type I censored samples. The maximum likelihood and the approximate maximum likelihood estimates are derived. The Bayes estimate and the associated credible interval are approximated by using Lindley's approximation and Markov Chain Monte Carlo using the importance sampling technique. Monte Carlo simulations are performed to compare the performances of the proposed methods....

Local degeneracy of Markov chain Monte Carlo methods

Kengo Kamatani (2014)

ESAIM: Probability and Statistics

Similarity:

We study asymptotic behavior of Markov chain Monte Carlo (MCMC) procedures. Sometimes the performances of MCMC procedures are poor and there are great importance for the study of such behavior. In this paper we call degeneracy for a particular type of poor performances. We show some equivalent conditions for degeneracy. As an application, we consider the cumulative probit model. It is well known that the natural data augmentation (DA) procedure does not work well for this model and the...

Hit and run as a unifying device

Hans C. Andersen, Persi Diaconis (2007)

Journal de la société française de statistique

Similarity:

We present a generalization of hit and run algorithms for Markov chain Monte Carlo problems that is ‘equivalent’ to data augmentation and auxiliary variables. These algorithms contain the Gibbs sampler and Swendsen-Wang block spin dynamics as special cases. The unification allows theorems, examples, and heuristics developed in one domain to illuminate parallel domains.

Numerical realization of the Bayesian inversion accelerated using surrogate models

Bérešová, Simona

Similarity:

The Bayesian inversion is a natural approach to the solution of inverse problems based on uncertain observed data. The result of such an inverse problem is the posterior distribution of unknown parameters. This paper deals with the numerical realization of the Bayesian inversion focusing on problems governed by computationally expensive forward models such as numerical solutions of partial differential equations. Samples from the posterior distribution are generated using the Markov...

On convergence in distribution of the Markov chain generated by the filter kernel induced by a fully dominated Hidden Markov Model

Thomas Kaijser

Similarity:

Consider a Hidden Markov Model (HMM) such that both the state space and the observation space are complete, separable, metric spaces and for which both the transition probability function (tr.pr.f.) determining the hidden Markov chain of the HMM and the tr.pr.f. determining the observation sequence of the HMM have densities. Such HMMs are called fully dominated. In this paper we consider a subclass of fully dominated HMMs which we call regular. A fully dominated,...

Application of MCMC to change point detection

Jaromír Antoch, David Legát (2008)

Applications of Mathematics

Similarity:

A nonstandard approach to change point estimation is presented in this paper. Three models with random coefficients and Bayesian approach are used for modelling the year average temperatures measured in Prague Klementinum. The posterior distribution of the change point and other parameters are estimated from the random samples generated by the combination of the Metropolis-Hastings algorithm and the Gibbs sampler.

On the Bayesian estimation for the stationary Neyman-Scott point processes

Jiří Kopecký, Tomáš Mrkvička (2016)

Applications of Mathematics

Similarity:

The pure and modified Bayesian methods are applied to the estimation of parameters of the Neyman-Scott point process. Their performance is compared to the fast, simulation-free methods via extensive simulation study. Our modified Bayesian method is found to be on average 2.8 times more accurate than the fast methods in the relative mean square errors of the point estimates, where the average is taken over all studied cases. The pure Bayesian method is found to be approximately as good...