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Antagonistic histone post-translational modifications improve the fidelity of epigenetic inheritance - a Bayesian perspective. [PDF]
Epigenetics ChromatinPrabhu BNB +3 more
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The Learning Rate Is Not a Constant: Sandwich-Adjusted Markov Chain Monte Carlo Simulation. [PDF]
Entropy (Basel)Vrugt JA, Diks CGH.
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Parallel MCMC algorithms: theoretical foundations, algorithm design, case studies. [PDF]
Trans Math ApplGlatt-Holtz NE +3 more
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<b>mhn</b>: a Python package for analyzing cancer progression with Mutual Hazard Networks. [PDF]
Bioinform AdvVocht S +8 more
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Privacy-hardened and hallucination-resistant synthetic data generation with logic-solvers. [PDF]
BioinformaticsBurgess MA +7 more
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EXAMPLES OF MARKOV CHAINS ON SPACES WITH MULTIPLICITIES
Ischia Group Theory 2010, 2011We show how to compute the spectra of several random walks that are invariant for the action of a group G in the case that the action is either not transitive or not multiplicity-free. This extends the classical analysis of stochastic processes developed by Diaconis et. al. in a Gelfand pair setting.
SCARABOTTI, Fabio, F. Tolli
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2018
In this chapter we present various examples of Markov chains. We will often use these examples in the sequel to illustrate the results we will develop. Most of our examples are derived from time series models or Monte Carlo simulation methods. Many time series models belong to the class of random iterative functions that are introduced in Section 2.1 ...
Randal Douc +3 more
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In this chapter we present various examples of Markov chains. We will often use these examples in the sequel to illustrate the results we will develop. Most of our examples are derived from time series models or Monte Carlo simulation methods. Many time series models belong to the class of random iterative functions that are introduced in Section 2.1 ...
Randal Douc +3 more
openaire +2 more sources
2000
By definition, a Markov chain is nothing but a probability vector (p i) together with a stochastic matrix P = (p ij). Mostly only P is given, and then it is tacitly assumed that one is interested in all starting distributions. Due to the law of total probability it suffices to study only the situations where one starts deterministically at a fixed but ...
E. Behrends
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By definition, a Markov chain is nothing but a probability vector (p i) together with a stochastic matrix P = (p ij). Mostly only P is given, and then it is tacitly assumed that one is interested in all starting distributions. Due to the law of total probability it suffices to study only the situations where one starts deterministically at a fixed but ...
E. Behrends
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1995
The revival of interest in Markov chains is based in part on their recent applicability in solving real world problems and in part on their ability to resolve issues in theoretical computer science. This paper presents three examples which are used to illustrate both parts: a Markov chain algorithm for estimating the tails of the bootstrap also ...
Diaconis, P., Holmes, Susan
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The revival of interest in Markov chains is based in part on their recent applicability in solving real world problems and in part on their ability to resolve issues in theoretical computer science. This paper presents three examples which are used to illustrate both parts: a Markov chain algorithm for estimating the tails of the bootstrap also ...
Diaconis, P., Holmes, Susan
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