Results 31 to 40 of about 193,674 (278)
Running Markov Chain without Markov Basis [PDF]
Harmony of Gröbner Bases and the Modern Industrial Society - The Second CREST-CSBM International Conference, 2012 18 ...
Hara, Hisayuki +2 more
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Information geometry of Markov Kernels: a survey
Frontiers in Physics, 2023 Information geometry and Markov chains are two powerful tools used in modern fields such as finance, physics, computer science, and epidemiology. In this survey, we explore their intersection, focusing on the theoretical framework.
Geoffrey Wolfer, Shun Watanabe
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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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Optimal choice of word length when comparing two Markov sequences using a χ 2-statistic
BMC Genomics, 2017 Background Alignment-free sequence comparison using counts of word patterns (grams, k-tuples) has become an active research topic due to the large amount of sequence data from the new sequencing technologies.
Xin Bai +4 more
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Markov Chains for Collaboration [PDF]
, 2014 Consider a system of \(n\) players in which each initially starts on a different team. At each time step, we select an individual winner and an individual loser randomly and the loser joins the winner's team.
Mena, Robert, Murray, Will
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Hitting Times and Probabilities for Imprecise Markov Chains [PDF]
, 2019 We consider the problem of characterising expected hitting times and hitting probabilities for imprecise Markov chains. To this end, we consider three distinct ways in which imprecise Markov chains have been defined in the literature: as sets of ...
De Bock, Jasper +2 more
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Stochastic Processes with Expected Stopping Time [PDF]
Logical Methods in Computer ScienceMarkov chains are the de facto finite-state model for stochastic dynamical systems, and Markov decision processes (MDPs) extend Markov chains by incorporating non-deterministic behaviors.
Krishnendu Chatterjee, Laurent Doyen
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Theoretical Computer Science, 2011 The article introduces constraint Markov chains as a new tool for specification. They are a generalization of interval Markov chains. Interval Markov chains extend Markov chains by labeling transitions with intervals, implying that each transition probability needs to be within the according interval.
Caillaud, Benoit +5 more
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Putting Markov Chains Back into Markov Chain Monte Carlo [PDF]
Journal of Applied Mathematics and Decision Sciences, 2007 Markov chain theory plays an important role in statistical inference both in the formulation of models for data and in the construction of efficient algorithms for inference. The use of Markov chains in modeling data has a long history, however the use of Markov chain theory in developing algorithms for statistical inference has only become popular ...
Barker, Richard J. +1 more
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Journal of Applied Mathematics, 2014 We study the strong law of large numbers for the frequencies of occurrence of states and ordered couples of states for countable Markov chains indexed by an infinite tree with uniformly bounded degree, which extends the corresponding results of countable
Bao Wang +3 more
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