Results 41 to 50 of about 50,759 (165)
MCMC‐driven importance samplers
Applied Mathematical Modelling, 2022 Monte Carlo sampling methods are the standard procedure for approximating complicated integrals of multidimensional posterior distributions in Bayesian inference. In this work, we focus on the class of Layered Adaptive Importance Sampling (LAIS) scheme, which is a family of adaptive importance samplers where Markov chain Monte Carlo algorithms are ...
F. Llorente +4 more
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MCMC and GLMs for estimating regression parameters: Evidence from non-life Egyptian insurance sector [PDF]
Journal of Humanities and Applied Social Sciences, 2019 Purpose – The purpose of this study is to estimate the linear regression parameters using two alternative techniques. First technique is to apply the generalized linear model (GLM) and the second technique is the Markov Chain Monte Carlo (MCMC) method ...
Mahmoud ELsayed, Amr Soliman
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pexm: A JAGS Module for Applications Involving the Piecewise Exponential Distribution
Journal of Statistical Software, 2021 In this study, we present a new module built for users interested in a programming language similar to BUGS to fit a Bayesian model based on the piecewise exponential (PE) distribution.
Vinícius D. Mayrink +2 more
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An Introduction to MCMC for Machine Learning [PDF]
Machine Learning, 2003 zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Christophe Andrieu +3 more
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Globally Centered Autocovariances in MCMC [PDF]
Journal of Computational and Graphical Statistics, 2022 Autocovariances are a fundamental quantity of interest in Markov chain Monte Carlo (MCMC) simulations with autocorrelation function (ACF) plots being an integral visualization tool for performance assessment. Unfortunately, for slow-mixing Markov chains, the empirical autocovariance can highly underestimate the truth.
Medha Agarwal, Dootika Vats
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TI-Stan: Adaptively Annealed Thermodynamic Integration with HMC †
Proceedings, 2019 We present a novel implementation of the adaptively annealed thermodynamic integration technique using Hamiltonian Monte Carlo (HMC). Thermodynamic integration with importance sampling and adaptive annealing is an especially useful method for estimating ...
R. Wesley Henderson, Paul M. Goggans
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An MCMC approach to classical estimation [PDF]
Journal of Econometrics, 2003 This paper studies computationally and theoretically attractive estimators referred here as to the Laplace type estimators (LTE). The LTE include means and quantiles of Quasi-posterior distributions defined as transformations of general (non-likelihood-based) statistical criterion functions, such as those in GMM, nonlinear IV, empirical likelihood ...
Chernozhukov, Victor, Hong, Han
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CoRR, 2014 Markov Chain Monte Carlo (MCMC) algorithms are a workhorse of probabilistic modeling and inference, but are difficult to debug, and are prone to silent failure if implemented naively. We outline several strategies for testing the correctness of MCMC algorithms.
Roger B. Grosse +1 more
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Open Physics, 2017 The coefficient of variation [CV] has several applications in applied statistics. So in this paper, we adopt Bayesian and non-Bayesian approaches for the estimation of CV under type-II censored data from extension exponential distribution [EED].
Bakoban Rana A.
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MCMC METHODS FOR DIFFUSION BRIDGES [PDF]
Stochastics and Dynamics, 2008 We present and study a Langevin MCMC approach for sampling nonlinear diffusion bridges. The method is based on recent theory concerning stochastic partial differential equations (SPDEs) reversible with respect to the target bridge, derived by applying the Langevin idea on the bridge pathspace.
Beskos, Alexandros +3 more
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