Results 41 to 50 of about 29,218,928 (220)
A hybrid adaptive MCMC algorithm in function spaces
, 2016 The preconditioned Crank-Nicolson (pCN) method is a Markov Chain Monte Carlo (MCMC) scheme, specifically designed to perform Bayesian inferences in function spaces. Unlike many standard MCMC algorithms, the pCN method can preserve the sampling efficiency
Hu, Zixi +3 more
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Using Markov chain Monte Carlo methods for estimating parameters with gravitational radiation data [PDF]
, 2001 We present a Bayesian approach to the problem of determining parameters for coalescing binary systems observed with laser interferometric detectors. By applying a Markov Chain Monte Carlo (MCMC) algorithm, specifically the Gibbs sampler, we demonstrate ...
A. Abramovici +43 more
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Phylogenetic Stochastic Mapping without Matrix Exponentiation
, 2014 Phylogenetic stochastic mapping is a method for reconstructing the history of trait changes on a phylogenetic tree relating species/organisms carrying the trait. State-of-the-art methods assume that the trait evolves according to a continuous-time Markov
Irvahn, Jan, Minin, Vladimir N.
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Научно-технический вестник информационных технологий, механики и оптикиIn biology, information about interactions between the proteins or genes under study can be represented as a biological graph. A connected subgraph, whose vertices perform a common biological function, is called an active module.
D. A. Usoltsev +4 more
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Bayesian Estimation of Mixed Multinomial Logit Models: Advances and Simulation-Based Evaluations
, 2019 Variational Bayes (VB) methods have emerged as a fast and computationally-efficient alternative to Markov chain Monte Carlo (MCMC) methods for scalable Bayesian estimation of mixed multinomial logit (MMNL) models.
Bansal, Prateek +4 more
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Sticky proposal densities for adaptive MCMC methods [PDF]
2016 IEEE Statistical Signal Processing Workshop (SSP), 2016 Monte Carlo (MC) methods are commonly used in Bayesian signal processing to address complex inference problems. The performance of any MC scheme depends on the similarity between the proposal (chosen by the user) and the target (which depends on the problem).
Martino L., Casarin R., Luengo D.
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Fast Compression of MCMC Output
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),
Nicolas Chopin, Gabriel Ducrocq
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A Bayesian approach on asymmetric heavy tailed mixture of factor analyzer
AIMS MathematicsA Mixture of factor analyzer (MFA) model is a powerful tool to reduce the number of free parameters in high-dimensional data through the factor-analyzer technique based on the covariance matrices.
Hamid Reza Safaeyan +4 more
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Accelerated MCMC for Satellite-Based Measurements of Atmospheric CO2
Remote Sensing, 2019 Markov Chain Monte Carlo (MCMC) is a powerful and promising tool for assessing the uncertainties in the Orbiting Carbon Observatory 2 (OCO-2) satellite’s carbon dioxide measurements.
Otto Lamminpää +6 more
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Friction-Identification of Harmonic Drive Joints Based on the MCMC Method
IEEE Access, 2022 Although harmonic drives have been adopted by all sorts of industrial environments, the mathematical expression of its dynamics has not yet been fully solved.
Qi Wang +5 more
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