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MCMC methods to approximate conditional predictive distributions
Computational Statistics & Data Analysis, 2006zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Maria J. Bayarri +2 more
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A MCMC-Method for Models with Continuous Latent Responses
Psychometrika, 2002This paper introduces a new technique for estimating the parameters of models with continuous latent data. Using the Rasch model as an example, it is shown that existing Bayesian techniques for parameter estimation, such as the Gibbs sampler, are not always easy to implement. Then, a new sampling-based Bayesian technique, called the DA-T-Gibbs sampler,
Maris, G.K.J., Maris, E.G.G.
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Bayesian function learning using MCMC methods
IEEE Transactions on Pattern Analysis and Machine Intelligence, 1998The paper deals with the problem of reconstructing a continuous 1D function from discrete noisy samples. The measurements may also be indirect in the sense that the samples may be the output of a linear operator applied to the function. Bayesian estimation provides a unified treatment of this class of problems. We show that a rigorous Bayesian solution
Paolo Magni +2 more
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Scaling Analysis of Delayed Rejection MCMC Methods
Methodology and Computing in Applied Probability, 2013zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Bédard, Mylène +2 more
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An auxiliary variable method for Langevin based MCMC algorithms
2016 IEEE Statistical Signal Processing Workshop (SSP), 2016Markov Chain Monte Carlo sampling algorithms are efficient Bayesian tools to explore complicated posterior distributions. However, sampling in large scale problems remains a challenging task since the Markov chain is very sensitive to the dependencies between the signal samples.
Marnissi, Yosra +3 more
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Introduction to Simulation and MCMC Methods
2011The purpose of this article is to provide an overview of Monte Carlo methods for generating variates from a target probability distribution that are based on Markov chains. These methods, called Markov chain Monte Carlo (MCMC) methods, are widely used to summarize complicated posterior distributions in Bayesian statistics and econometrics. This article
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MCMC methods for discrete source separation
AIP Conference Proceedings, 2001Source separation consists in recovering signals mixed by an unknown transmission channel. Likelihood and information theory or higher order statistics can be used to perform the separation. This paper proposes a Bayesian approach to the problem of an instantaneous linear mixing, considering the source signals are discrete valued.
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MCMC methods for sampling function space
2009Applied mathematics is concerned with developing models with predictive capability, and with probing those models to obtain qualitative and quantitative insight into the phenomena being modelled. Statistics is data-driven and is aimed at the development of methodologies to optimize the information derived from data.
Beskos, Alexandros, Stuart, Andrew
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Optimization of Paintbrush Rendering of Images by Dynamic MCMC Methods
2001We have developed a new stochastic image rendering method for the compression, description and segmentation of images. This paintbrush-like image transformation is based on a random searching to insert brush-strokes into a generated image at decreasing scale of brush-sizes, without predefined models or interaction. We introduced a sequential multiscale
Tamás Szirányi, Zoltán Tóth
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An Approximate MCMC Method for Convex Hulls
2019Markov chain Monte Carlo (MCMC) is an extremely popular class of algorithms for computing summaries of posterior distributions. One problem for MCMC in the so-called Big Data regime is the growing computational cost of most MCMC algorithms. Most popular and basic MCMC algorithms, like Metropolis-Hastings algorithm (MH) and Gibbs algorithm, have to take
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