Results 11 to 20 of about 66,786 (305)
Monthly Notices of the Royal Astronomical Society, 2021 ABSTRACTWe introduce Bilby-MCMC, a Markov chain Monte Carlo sampling algorithm tuned for the analysis of gravitational waves from merging compact objects. Bilby-MCMC provides a parallel-tempered ensemble Metropolis-Hastings sampler with access to a block-updating proposal library including problem-specific and machine learning proposals. We demonstrate
G Ashton, C Talbot
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Orthogonal MCMC algorithms [PDF]
2014 IEEE Workshop on Statistical Signal Processing (SSP), 2014 Monte Carlo (MC) methods are widely used in signal processing, machine learning and stochastic optimization. A well-known class of MC methods are Markov Chain Monte Carlo (MCMC) algorithms. In this work, we introduce a novel parallel interacting MCMC scheme, where the parallel chains share information using another MCMC technique working on the entire ...
Luca Martino +4 more
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Adaptive Metropolis-coupled MCMC for BEAST 2 [PDF]
PeerJ, 2020 With ever more complex models used to study evolutionary patterns, approaches that facilitate efficient inference under such models are needed. Metropolis-coupled Markov chain Monte Carlo (MCMC) has long been used to speed up phylogenetic analyses and to
Nicola F. Müller, Remco R. Bouckaert
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This paper is intended to appear as a chapter for the Handbook of Markov Chain Monte Carlo. The goal of this chapter is to unify various problems at the intersection of Markov chain Monte Carlo (MCMC) and machine learning$\unicode{x2014}$which includes black-box variational inference, adaptive MCMC, normalizing flow construction and transport-assisted ...
Alexandre Bouchard-Côté +3 more
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MCMC Techniques for Parameter Estimation of ODE Based Models in Systems Biology
Frontiers in Applied Mathematics and Statistics, 2019 Ordinary differential equation systems (ODEs) are frequently used for dynamical system modeling in many science fields such as economics, physics, engineering, and systems biology.
Holger Frohlich
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Annual Review of Statistics and Its Application, 2022 Markov chain Monte Carlo is the engine of modern Bayesian statistics, being used to approximate the posterior and derived quantities of interest. Despite this, the issue of how the output from a Markov chain is postprocessed and reported is often overlooked.
South, Leah +3 more
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Cyclical MCMC is a novel MCMC framework recently proposed by Zhang et al. (2019) to address the challenge posed by high-dimensional multimodal posterior distributions like those arising in deep learning. The algorithm works by generating a nonhomogeneous Markov chain that tracks -- cyclically in time -- tempered versions of the target distribution.
Liwei Wang +3 more
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Tehran Stock Exchange Return Forecasting: Comparison of Bayesian, Exponential Smoothing and Box Jenkins Approaches [PDF]
فصلنامه پژوهشهای اقتصادی ایران, 2022 Stock returns forecasting is very crucial for investors, share-holders and arbiters. Different methods have been developed for this purpose. In general, there are four methods of forecasting in stock markets, which are; Technical Analysis, Fundamental ...
Mojtaba Rostami +1 more
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Parameter Estimation in Mass Balance Model Applied in Fixed Bed Adsorption Using the Markov Chain Monte Carlo Method [PDF]
Journal of Heat and Mass Transfer Research, 2022 In this work, a mathematical model is adopted to predict the breakthrough curve in a fixed bed adsorption process, neglecting radial dispersion effects in the bed, with properties such as interstitial velocity and porosity being constant, linear ...
Rhaisa Tavares +6 more
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Journal of Open Research Software, 2021 The North American Breeding Bird Survey (BBS) is the primary ecological monitoring program used to assess the population, status, and trends of North American birds.
Brandon P. M. Edwards, Adam C. Smith
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