Results 31 to 40 of about 112,632 (333)
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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Multimodal nested sampling: an efficient and robust alternative to Markov Chain Monte Carlo methods for astronomical data analyses [PDF]
, 2007 In performing a Bayesian analysis of astronomical data, two difficult problems often emerge. First, in estimating the parameters of some model for the data, the resulting posterior distribution may be multimodal or exhibit pronounced (curving ...
F. Feroz, M. Hobson
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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
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Application of Markov chain Monte carlo method in Bayesian statistics
MATEC Web of Conferences, 2016 In statistical inference methods, bayesian method is a method of great influence. This paper introduces the basic idea of the bayesian method. However, the widespread popularity of MCMC samplers is largely due to their impact on solving statistical ...
Zhao Qi
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Markov Chain Monte Carlo Methods for Bayesian Data Analysis in Astronomy [PDF]
, 2017 Markov Chain Monte Carlo based Bayesian data analysis has now become the method of choice for analyzing and interpreting data in almost all disciplines of science.
Sanjib Sharma
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Advanced Science, EarlyView.On the Qinghai–Tibetan Plateau, microbial carbon use efficiency (CUE) peaks at intermediate soil organic carbon levels and declines thereafter. In carbon‐rich soils, the formation of stable mineral‐associated organic carbon is decoupled from microbial CUE.
Yuting Wang +8 more
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Applied Sciences, 2018 Establishing fast and reversible photon multiple scattering algorithms remains a modeling challenge for optical diagnostics and noise reduction purposes, especially when the scattering happens within the intermediate scattering regime.
Shangze Yang +3 more
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Challenges in Markov Chain Monte Carlo for Bayesian Neural Networks [PDF]
Statistical Science, 2019 Markov chain Monte Carlo (MCMC) methods have not been broadly adopted in Bayesian neural networks (BNNs). This paper initially reviews the main challenges in sampling from the parameter posterior of a neural network via MCMC. Such challenges culminate to
T. Papamarkou +3 more
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