Results 11 to 20 of about 54,345 (310)
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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CoRR, 2020 Markov Chain Monte Carlo (MCMC) algorithms ubiquitously employ complex deterministic transformations to generate proposal points that are then filtered by the Metropolis-Hastings-Green (MHG) test. However, the condition of the target measure invariance puts restrictions on the design of these transformations.
Kirill Neklyudov, Max Welling
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Uncertainty assessment for the Bayesian updating process of concrete strength properties
Acta Polytechnica CTU Proceedings, 2022 Reassessment of infrastructure buildings has become an essential approach to deal with increasing traffic loads on ageing infrastructure buildings and to verify the service-life of those structures.
Matthias Haslbeck +3 more
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Hierarchical Bayesian analysis of racehorse running ability and jockey skills
International Journal of Computer Science in Sport, 2023 In this paper, we proposed a new method of evaluating horse ability and jockey skills in horse racing. In the proposed method, we aimed to estimate unobservable individual effects of horses and jockeys simultaneously with regression coefficients for ...
Nakakita M., Nakatsuma T.
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Accelerating MCMC algorithms [PDF]
WIREs Computational Statistics, 2018 Markov chain Monte Carlo algorithms are used to simulate from complex statistical distributions by way of a local exploration of these distributions. This local feature avoids heavy requests on understanding the nature of the target, but it also potentially induces a lengthy exploration of this target, with a requirement on the number of simulations ...
Robert, Christian P. +3 more
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A STUDY OF GENERALIZED LINEAR MIXED MODEL FOR COUNT DATA USING HIERARCHICAL BAYES METHOD
Media Statistika, 2022 Poisson Log-Normal Model is one of the hierarchical mixed models that can be used for count data. Several estimation methods can be used to estimate the model parameters.
Etis Sunandi +2 more
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Journal of Statistical Software, 2021 We introduce the new package dmbc that implements a Bayesian algorithm for clustering a set of binary dissimilarity matrices within a model-based framework.
Sergio Venturini, Raffaella Piccarreta
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spNNGP R Package for Nearest Neighbor Gaussian Process Models
Journal of Statistical Software, 2022 This paper describes and illustrates functionality of the spNNGP R package. The package provides a suite of spatial regression models for Gaussian and non-Gaussian pointreferenced outcomes that are spatially indexed. The package implements several Markov
Andrew O. Finley +2 more
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Fast Compression of MCMC Output [PDF]
Entropy, 2021 We propose cube thinning, a novel method for compressing the output of an MCMC (Markov chain Monte Carlo) algorithm when control variates are available. It allows resampling of the initial MCMC sample (according to weights derived from control variates), while imposing equality constraints on the averages of these control variates, using the cube ...
Nicolas Chopin, Gabriel Ducrocq
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Population pharmacokinetics analysis in Lixoft Monolix softwares
Фармакокинетика и Фармакодинамика, 2022 The article has discussed a step-by-step algorithm for developing population pharmacokinetics models in the Lixoft Monolix software. Features of population pharmacokinetics study’s methodology have described.
A. I. Platova
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