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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 Annals of Probability, 1975 We propose a control problem in which we minimize the expected hitting time of a fixed state in an arbitrary Markov chains with countable state space. A Markovian optimal strategy exists in all cases, and the value of this strategy is the unique solution of a nonlinear equation involving the transition function of the Markov chain.
Kesten, Harry, Spitzer, Frank
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New Zealand Journal of Mathematics, 2021 We introduce the space of virtual Markov chains (VMCs) as a projective limit of the spaces of all finite state space Markov chains (MCs), in the same way that the space of virtual permutations is the projective limit of the spaces of all permutations of finite sets.We introduce the notions of virtual initial distribution (VID) and a virtual transition ...
Evans, Steven, Jaffe, Adam Q.
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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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, 2018 This book covers the classical theory of Markov chains on general state-spaces as well as many recent developments. The theoretical results are illustrated by simple examples, many of which are taken from Markov Chain Monte Carlo methods. The book is self-contained, while all the results are carefully and concisely proven.
Douc, Randal +3 more
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Approximating Markov chains. [PDF]
Proceedings of the National Academy of Sciences, 1992 A common framework of finite state approximating Markov chains is developed for discrete time deterministic and stochastic processes. Two types of approximating chains are introduced: (i) those based on stationary conditional probabilities (time averaging) and (ii) transient, based on the percentage of the Lebesgue measure of the image of cells ...
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Metastable Markov chains [PDF]
53rd IEEE Conference on Decision and Control, 2014 In this paper, we discuss the dynamics of metastable systems. Such systems exhibit interesting long-living behaviors from which they are guaranteed to inevitably escape (e.g., eventually arriving at a distinct failure or success state). At the heart of this work, we emphasize (1) that for our goals, hybrid systems can be approximated as Markov Decision
Cenk Oguz Saglam, Katie Byl
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Markov chain Monte Carlo methods for state-space models with point process observations [PDF]
, 2012 This letter considers how a number of modern Markov chain Monte Carlo (MCMC) methods can be applied for parameter estimation and inference in state-space models with point process observations.
Niranjan, Mahesan +2 more
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Probabilistic Markov chain modelling of photonic crystal surface emitting lasers [PDF]
, 2023 Probabilistic Markov chain modeling of photonic crystal surface emitting lasers (PCSELs) is reported. This simulation links the scattering parameters of the photonic crystal (PC) and device level losses of the PCSEL.
Liu, Jingzhao +4 more
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Conditioning an additive functional of a markov chain to stay non-negative. I, Survival for a long time [PDF]
, 2005 Let (X-t)(t >= 0) be a continuous-time irreducible Markov chain on a finite state space E, let v be a map v: E -> R \ {0}, and let (phi(t))(t >= 0) be an additive functional defined by phi(t) = integral(0)(t)(X-s) ds.
Warren, Jon +2 more
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