Results 31 to 40 of about 85,479 (297)
Multilevel Monte Carlo for continuous time Markov chains, with applications in biochemical kinetics [PDF]
, 2012 We show how to extend a recently proposed multi-level Monte Carlo approach to the continuous time Markov chain setting, thereby greatly lowering the computational complexity needed to compute expected values of functions of the state of the system to a ...
Anderson, David +3 more
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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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Hybrid Monte Carlo on Hilbert spaces [PDF]
, 2011 The Hybrid Monte Carlo (HMC) algorithm provides a framework for sampling from complex, high-dimensional target distributions. In contrast with standard Markov chain Monte Carlo (MCMC) algorithms, it generates nonlocal, nonsymmetric moves in the state ...
Beskos, A +15 more
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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
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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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Differentially Private Markov Chain Monte Carlo
CoRR, 2019 Recent developments in differentially private (DP) machine learning and DP Bayesian learning have enabled learning under strong privacy guarantees for the training data subjects. In this paper, we further extend the applicability of DP Bayesian learning by presenting the first general DP Markov chain Monte Carlo (MCMC) algorithm whose privacy ...
Heikkilä, Mikko +3 more
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