Results 21 to 30 of about 221,738 (314)

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

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

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

Stereographic Markov chain Monte Carlo [PDF]

The Annals of Statistics
80 pages, 20 ...
Yang, Jun, Łatuszyński, Krzysztof, Roberts, Gareth O.   +2 more
openaire   +3 more sources

A response to Yu et al. "A forward-backward fragment assembling algorithm for the identification of genomic amplification and deletion breakpoints using high-density single nucleotide polymorphism (SNP) array", BMC Bioinformatics 2007, 8: 145

BMC Bioinformatics, 2007
Background Yu et al. (BMC Bioinformatics 2007,8: 145+) have recently compared the performance of several methods for the detection of genomic amplification and deletion breakpoints using data from high-density single nucleotide polymorphism arrays.
Diaz-Uriarte Ramon, Rueda Oscar M
doaj   +1 more source

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

HYDRA: a Java library for Markov Chain Monte Carlo

Journal of Statistical Software, 2002
Hydra is an open-source, platform-neutral library for performing Markov Chain Monte Carlo. It implements the logic of standard MCMC samplers within a framework designed to be easy to use, extend, and integrate with other software tools. In this paper, we
Gregory R. Warnes
doaj   +3 more sources

Block-Wisely Supervised Network Pruning with Knowledge Distillation and Markov Chain Monte Carlo

Applied Sciences, 2022
Structural network pruning is an effective way to reduce network size for deploying deep networks to resource-constrained devices. Existing methods mainly employ knowledge distillation from the last layer of network to guide pruning of the whole network,
Huidong Liu, Fang Du, Lijuan Song, Zhenhua Yu   +3 more
doaj   +1 more source

Statistical Inference for Partially Observed Markov Processes via the R Package pomp

Journal of Statistical Software, 2016
Partially observed Markov process (POMP) models, also known as hidden Markov models or state space models, are ubiquitous tools for time series analysis.
Aaron A. King, Dao Nguyen, Edward L. Ionides   +2 more
doaj   +1 more source

An efficient interpolation technique for jump proposals in reversible-jump Markov chain Monte Carlo calculations [PDF]

Royal Society Open Science, 2015
Selection among alternative theoretical models given an observed dataset is an important challenge in many areas of physics and astronomy. Reversible-jump Markov chain Monte Carlo (RJMCMC) is an extremely powerful technique for performing Bayesian model ...
W. M. Farr, I. Mandel, D. Stevens
doaj   +1 more source