Results 21 to 30 of about 61,070 (252)
Statistical Inference on Simple Step-Stress Accelerated Life Testing for Gompertz Distribution Under .Progressive Type-II Censoring [PDF]
Maǧallaẗ Al-Buḥūṯ Al-TiǧāriyyaẗWe consider a simple step-stress model under the Gompertz distribution (GD) when the available data are type-II progressive censored. The cumulative exposure model is assumed when the lifetime of test units follows a Gompertz distribution.
السيد وليد شعبان عبدالمنتصر +2 more
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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
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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 +5 more
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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
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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
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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
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The Bootstrap and Markov-Chain Monte Carlo [PDF]
Journal of Biopharmaceutical Statistics, 2011 This note concerns the use of parametric bootstrap sampling to carry out Bayesian inference calculations. This is only possible in a subset of those problems amenable to Markov-Chain Monte Carlo (MCMC) analysis, but when feasible the bootstrap approach offers both computational and theoretical advantages.
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Stereographic Markov chain Monte Carlo [PDF]
The Annals of Statistics80 pages, 20 ...
Yang, Jun +2 more
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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 +2 more
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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 +3 more
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