Results 241 to 250 of about 132,359 (288)

A MCMC method for resolving two person mixtures

Science & Justice, 2008
In this paper a Monte Carlo Markov Chain (MCMC) method for resolving DNA mixtures containing at most four peaks per locus into a major and a minor contributor is presented. Unlike previous methods, this method can provide posterior probability assessments of the most probable genotype and a likely range for the mixing proportion. The proposed method is
James M Curran
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Supervised classification using MCMC methods

2000 IEEE International Conference on Acoustics, Speech, and Signal Processing. Proceedings (Cat. No.00CH37100), 2002
This paper addresses the problem of supervised classification using general Bayesian learning. General Bayesian learning consists of estimating the unknown class-conditional densities from a set of labelled samples. However, the estimation requires to evaluate intractable multidimensional integrals.
M. Davy, C. Doncarli, J.-Y. Tourneret
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Scaling Analysis of Delayed Rejection MCMC Methods

Methodology and Computing in Applied Probability, 2013
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Bédard, Mylène, Douc, Randal, Moulines, Eric   +2 more
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Classification of Digital Modulations Using MCMC Methods

Monte Carlo Methods and Applications, 2001
This paper presents a simulation study of the Markov chain Monte Carlo (MCMC) technique applied to classification of digital modulations. The latter problem consists of determining the underlying symbol constellation of transmitted signals from observed noisy measurements.
Lesage, Stéphane, Tourneret, Jean-Yves, Djurić, Petar M.   +2 more
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Parallel MCMC methods for global optimization

Monte Carlo Methods and Applications, 2019
Abstract We introduce a parallel scheme for simulated annealing, a widely used Markov chain Monte Carlo (MCMC) method for optimization. Our method is constructed and analyzed under the classical framework of MCMC. The benchmark function for optimization is used for validation and verification of the parallel scheme.
Zhang, Lihao, Ye, Zeyang, Deng, Yuefan
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Bayesian function learning using MCMC methods

IEEE Transactions on Pattern Analysis and Machine Intelligence, 1998
The 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, BELLAZZI, RICCARDO, DE NICOLAO, GIUSEPPE   +2 more
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MCMC methods for discrete source separation

AIP Conference Proceedings, 2001
Source 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

2009
Applied 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, 2002
This 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, Michel Doisy, Jean- Yves Tourneret   +2 more
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MCMC methods to approximate conditional predictive distributions

Computational Statistics & Data Analysis, 2006
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Bayarri, M. J., Castellanos, M. E., Morales, J.   +2 more
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