Results 21 to 30 of about 198,078 (301)

A full bayesian approach for boolean genetic network inference. [PDF]

PLoS ONE, 2014
Boolean networks are a simple but efficient model for describing gene regulatory systems. A number of algorithms have been proposed to infer Boolean networks.
Shengtong Han, Raymond K W Wong, Thomas C M Lee, Linghao Shen, Shuo-Yen R Li, Xiaodan Fan   +5 more
doaj   +1 more source

Reflections on Bayesian inference and Markov chain Monte Carlo

Canadian Journal of Statistics, Volume 50, Issue 4, Page 1213-1227, December 2022., 2022
Abstract Bayesian inference and Markov chain Monte Carlo methods are vigorous areas of statistical research. Here we reflect on some recent developments and future directions in these fields. Résumé L'inférence bayésienne et les méthodes de Monte‐Carlo par chaîne de Markov sont des domaines dynamiques de la recherche statistique.
Radu V. Craiu, Paul Gustafson, Jeffrey S. Rosenthal   +2 more
wiley   +1 more source

Parallel Markov chain Monte Carlo - bridging the gap to high-performance Bayesian computation in animal breeding and genetics

Genetics Selection Evolution, 2012
Background Most Bayesian models for the analysis of complex traits are not analytically tractable and inferences are based on computationally intensive techniques.
Wu Xiao-Lin, Sun Chuanyu, Beissinger Timothy M, Rosa Guilherme JM, Weigel Kent A, Gatti Natalia de, Gianola Daniel   +6 more
doaj   +1 more source

Canadian contributions to environmetrics

Canadian Journal of Statistics, Volume 50, Issue 4, Page 1355-1386, December 2022., 2022
Abstract This article focuses on the importance of collaboration in statistics by Canadian researchers and highlights the contributions that Canadian statisticians have made to many research areas in environmetrics. We provide a discussion about different vehicles that have been developed for collaboration by Canadians in the environmetrics context as ...
Charmaine B. Dean, Abdel H. El‐Shaarawi, Sylvia R. Esterby, Joanna Mills Flemming, Richard D. Routledge, Stephen W. Taylor, Douglas G. Woolford, James V. Zidek, Francis W. Zwiers   +8 more
wiley   +1 more source

A Markov Chain Monte Carlo Algorithm for Spatial Segmentation

Information, 2021
Spatial data are very often heterogeneous, which indicates that there may not be a unique simple statistical model describing the data. To overcome this issue, the data can be segmented into a number of homogeneous regions (or domains). Identifying these
Nishanthi Raveendran, Georgy Sofronov
doaj   +1 more source

Information-Geometric Markov Chain Monte Carlo Methods Using Diffusions

Entropy, 2014
Recent work incorporating geometric ideas in Markov chain Monte Carlo is reviewed in order to highlight these advances and their possible application in a range of domains beyond statistics. A full exposition of Markov chains and their use in Monte Carlo
Samuel Livingstone, Mark Girolami
doaj   +1 more source

IMPLEMENTASI MARKOV CHAIN MONTE CARLO PADA PENDUGAAN HYPERPARAMETER REGRESI PROSES GAUSSIAN

Media Statistika, 2011
This paper studies the implementation of Markov Chain Monte Carlo on estimating the hyperparameter of Gaussian process. Metropolish-Hasting (MH) algorithm is used to generate the random samples from the posterior distribution that can not be generated by
Moch. Abdul Mukid, Sugito Sugito
doaj   +1 more source

On nonlinear Markov chain Monte Carlo

Bernoulli, 2011
Published in at http://dx.doi.org/10.3150/10-BEJ307 the Bernoulli (http://isi.cbs.nl/bernoulli/) by the International Statistical Institute/Bernoulli Society (http://isi.cbs.nl/BS/bshome.htm)
Arnaud Doucet, Ajay Jasra, Pierre Del Moral, Pierre Del Moral, Christophe Andrieu   +4 more
openaire   +7 more sources

Stochastic Gradient Markov Chain Monte Carlo [PDF]

Journal of the American Statistical Association, 2021
Markov chain Monte Carlo (MCMC) algorithms are generally regarded as the gold standard technique for Bayesian inference. They are theoretically well-understood and conceptually simple to apply in practice. The drawback of MCMC is that in general performing exact inference requires all of the data to be processed at each iteration of the algorithm.
Christopher Nemeth, Paul Fearnhead
openaire   +5 more sources

Markov Chain Monte Carlo Methods for Estimating Systemic Risk Allocations

Risks, 2020
In this paper, we propose a novel framework for estimating systemic risk measures and risk allocations based on Markov Chain Monte Carlo (MCMC) methods. We consider a class of allocations whose jth component can be written as some risk measure of the jth
Takaaki Koike, Marius Hofert
doaj   +1 more source