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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
MAGNI, PAOLO +2 more
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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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Changepoint detection using reversible jump MCMC methods
IEEE International Conference on Acoustics Speech and Signal Processing, 2002This paper addresses the problem of SAR image segmentation by using reversible jump MCMC sampling. The SAR image segmentation problem is formulated as a Bayesian estimation problem. The reversible jump MCMC algorithm is then used to generate samples distributed according to the joint posterior distribution of the unknown parameters. These samples allow
S. Suparman +2 more
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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.
Bayarri, M. J. +2 more
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The ADT evaluation method based on MCMC
2011 IEEE MTT-S International Microwave Workshop Series on Innovative Wireless Power Transmission: Technologies, Systems, and Applications, 2011This paper proposes an accelerated degradation testing (ADT) evaluation method based on Markov Chain Monte Carlo (MCMC) method. Firstly the degradation model, reliability model and accelerated model of ADT are introduced; secondly, with the information above, the ADT evaluation method based on MCMC is proposed; Thirdly, the evaluation results of this ...
Lizhi Wang +3 more
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Interpret Model Complexity: Trans-Dimensional MCMC Method
2020In the previous chapters, we witness the power of statistical inverse methods that used to sample from the posterior distribution of earth model parameters given the observed azimuthal resistivity measurements. The statistical inversion resolves the local minimum problem in the deterministic methods and tells the uncertainty of model parameters via the
Qiuyang Shen +4 more
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Estimating heterogeneous transmission with multiple infectives using MCMC methods
Statistics in Medicine, 2003AbstractWe developed a general procedure for estimating the transmission probability adjusting for covariates when susceptibles are exposed to several infectives concurrently and taking correlation within transmission units into account. The procedure is motivated by a study estimating efficacy of pertussis vaccination based on the secondary attack ...
Haitao, Chu +2 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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Marginal Likelihood Calculation with MCMC Methods
2013Markov Chain Monte Carlo (MCMC) methods have revolutionised Bayesian data analysis over the years by making the direct computation of posterior probability densities feasible on modern workstations. However, the calculation of the prior predictive, the marginal likelihood, has proved to be notoriously difficult with standard techniques. In this chapter
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