Results 31 to 40 of about 146,110 (315)
Average-Based Fuzzy Time Series Markov Chain Based on Frequency Density Partitioning
Journal of Applied Mathematics, 2023 Fuzzy time series (FTS) is one of the forecasting methods that has been developed until now. The fuzzy time series is a forecasting method that uses the concept of fuzzy logic, which Song and Chissom first introduced.
Susilo Hariyanto +3 more
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Polynomial Recurrence of Time-inhomogeneous Markov Chains
Austrian Journal of Statistics, 2023 This paper is devoted to establishing conditions that guarantee the existence of a p-th moment of the time it takes for a timeinhomogeneous Markov chain to hit some set C.
Vitaliy Golomoziy, Olha Moskanova
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Distributed Markov Chains [PDF]
, 2015 The formal verification of large probabilistic models is challenging. Exploiting the concurrency that is often present is one way to address this problem. Here we study a class of communicating probabilistic agents in which the synchronizations determine the probability distribution for the next moves of the participating agents.
Ratul Saha +4 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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, 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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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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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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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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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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Segregating Markov Chains [PDF]
Journal of Theoretical Probability, 2017 Dealing with finite Markov chains in discrete time, the focus often lies on convergence behavior and one tries to make different copies of the chain meet as fast as possible and then stick together. There is, however, a very peculiar kind of discrete finite Markov chain, for which two copies started in different states can be coupled to meet almost ...
Timo Hirscher, Anders Martinsson
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