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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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Remote Sensing, 2020 Cellular Automata models are used for simulating spatial distributions and Markov Chain models are used for simulating temporal changes. The main aim of this study is to investigate the effect of urban growth on Faisalabad.
A. Tariq, Hong Shu
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Using fuzzy time series with and without markov chain: to forecast of edible bird nest exported from Indonesia [PDF]
E3S Web of Conferences, 2022 Edible bird nest (EBN) were traditional medicine consumed by the Tiongkok. This study compared two-algorithm method. Fuzzy time series and Markov chain as forecast method the number of bird nest exported from Indonesia.
Sjofjan Osfar, Adli Danung Nur
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Logical Methods in Computer Science, 2007 We consider qualitative and quantitative verification problems for infinite-state Markov chains. We call a Markov chain decisive w.r.t. a given set of target states F if it almost certainly eventually reaches either F or a state from which F can no longer be reached.
Abdulla, Parosh Aziz +2 more
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Stochastic Gradient Markov Chain Monte Carlo [PDF]
Journal of the American Statistical Association, 2019 Markov chain Monte Carlo (MCMC) algorithms are generally regarded as the gold standard technique for Bayesian inference. They are theoretically well-understood and conceptually simple to apply in practice.
C. Nemeth, P. Fearnhead
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Prediction of passenger movement indexes using Holt Winters Markov chain model [PDF]
Muṭāli̒āt-i Mudīriyyat-i Ṣan̒atī, 2016 In order to predict the values of passengers movement indexes which enjoy seasonal changes, this research uses Holt Winters Markov chain model, which is a combination of Markov chain and Holt Winters models.
Fatemeh haghighat, Fariborz Golai
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Unbiased Markov chain Monte Carlo methods with couplings
Journal of the Royal Statistical Society: Series B (Statistical Methodology), 2020 Markov chain Monte Carlo (MCMC) methods provide consistent approximations of integrals as the number of iterations goes to ∞. MCMC estimators are generally biased after any fixed number of iterations.
P. Jacob, J. O'Leary, Y. Atchadé
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Modeling Investment Trends: A Logarithmic-Modified Markov Chain Approach
Journal of Statistical Theory and Applications (JSTA), 2020 The study aimed at stabilizing the changing variance using the logarithmic transformation to achieve a significant proportion of stability and a faster rate of convergence of the steady state transition probability in Markov chains.
Imoh Udo Moffat +2 more
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Rudarsko-geološko-naftni Zbornik, 2021 The selection of a drill bit is an essential issue in well planning. Furthermore, identification and evaluation of sedimentary rocks before well drilling plays a crucial role in choosing the drill bit.
Afsaneh Ghaffari Rad +3 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 ...
Anders Martinsson +3 more
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