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Efficient parameter generation for constrained models using MCMC
Scientific Reports, 2023 Mathematical models of complex systems rely on parameter values to produce a desired behavior. As mathematical and computational models increase in complexity, it becomes correspondingly difficult to find parameter values that satisfy system constraints.
Natalia Kravtsova +2 more
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Hybrid Monte Carlo on Hilbert spaces [PDF]
, 2011 The Hybrid Monte Carlo (HMC) algorithm provides a framework for sampling from complex, high-dimensional target distributions. In contrast with standard Markov chain Monte Carlo (MCMC) algorithms, it generates nonlocal, nonsymmetric moves in the state ...
Beskos, A +15 more
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Markov chain Monte Carlo for integrated face image analysis [PDF]
, 2014 This PhD thesis is about the integration of different methods to fit a statistical model of human faces to a single image. I propose to take a probabilistic view on the problem and implement and evaluate an integrative framework for face image ...
Schönborn, Sandro
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The Rise of Markov Chain Monte Carlo Estimation for Psychometric Modeling
Journal of Probability and Statistics, 2009 Markov chain Monte Carlo (MCMC) estimation strategies represent a powerful approach to estimation in psychometric models. Popular MCMC samplers and their alignment with Bayesian approaches to modeling are discussed.
Roy Levy
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Probabilistic programming in Python using PyMC3 [PDF]
PeerJ Computer Science, 2016 Probabilistic programming allows for automatic Bayesian inference on user-defined probabilistic models. Recent advances in Markov chain Monte Carlo (MCMC) sampling allow inference on increasingly complex models.
John Salvatier +2 more
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New Computer Experiment Designs Using Continuum Random Cluster Point Process
International Journal of Analysis and Applications, 2023 In this paper, we propose a new approach for building computer experiment designs using the continuum random cluster point process, also referred to as the connected component Markov point process.
Hichem Elmossaoui, Nadia Oukid
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APT-MCMC, a C++/Python implementation of Markov Chain Monte Carlo for parameter identification [PDF]
Computers & Chemical Engineering, 2018 The inverse problem associated with fitting parameters of an ordinary differential equation (ODE) system to data is nonlinear and multimodal, which is of great challenge to gradient-based optimizers. Markov Chain Monte Carlo (MCMC) techniques provide an alternative approach to solving these problems and can escape local minima by design.
Li Ang Zhang +5 more
openaire +2 more sources
EPJ Web of Conferences, 2013 Bayesian inference often requires integrating some function with respect to a posterior distribution. Monte Carlo methods are sampling algorithms that allow to compute these integrals numerically when they are not analytically tractable.
Bardenet Rémi
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A Bayesian model for binary Markov chains
International Journal of Mathematics and Mathematical Sciences, 2004 This note is concerned with Bayesian estimation of the transition probabilities of a binary Markov chain observed from heterogeneous individuals. The model is founded on the Jeffreys' prior which allows for transition probabilities to be correlated.
Souad Assoudou, Belkheir Essebbar
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Variance bounding and geometric ergodicity of Markov chain Monte Carlo kernels for approximate Bayesian computation [PDF]
, 2014 Approximate Bayesian computation has emerged as a standard computational tool when dealing with intractable likelihood functions in Bayesian inference. We show that many common Markov chain Monte Carlo kernels used to facilitate inference in this setting
Łatuszyński, Krzysztof, Lee, Anthony
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