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Proceedings Joint 9th IFSA World Congress and 20th NAFIPS International Conference (Cat. No. 01TH8569), 2002
We first review some of the basic results of finite Markov chains based on probability theory, then we present fuzzy finite Markov chains based on possibility theory, and compare the results of the two theories. Then we introduce finite horizon Markovian decision processes based on fuzzy Markov chains and study an example in detail showing our solution
Yoichi Hayashi +2 more
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We first review some of the basic results of finite Markov chains based on probability theory, then we present fuzzy finite Markov chains based on possibility theory, and compare the results of the two theories. Then we introduce finite horizon Markovian decision processes based on fuzzy Markov chains and study an example in detail showing our solution
Yoichi Hayashi +2 more
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2001
Abstract Show that any sequence of independent random variables taking values in the countable set S is a Markov chain. Under what condition is this chain homogeneous? A die is rolled repeatedly. Which of the following are Markov chains? For those that are, supply the transition matrix.
Geoffrey R Grimmett, David R Stirzaker
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Abstract Show that any sequence of independent random variables taking values in the countable set S is a Markov chain. Under what condition is this chain homogeneous? A die is rolled repeatedly. Which of the following are Markov chains? For those that are, supply the transition matrix.
Geoffrey R Grimmett, David R Stirzaker
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Revenue Management Under the Markov Chain Choice Model
Operational Research, 2017We consider revenue management problems when customers choose among the offered products according to the Markov chain choice model. In this choice model, a customer arrives into the system to purchase a particular product.
Jacob B. Feldman, Huseyin Topaloglu
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, 2018
The transitional Markov chain Monte Carlo (TMCMC) is one of the efficient algorithms for performing Markov chain Monte Carlo (MCMC) in the context of Bayesian uncertainty quantification in parallel...
Stephen Wu +3 more
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The transitional Markov chain Monte Carlo (TMCMC) is one of the efficient algorithms for performing Markov chain Monte Carlo (MCMC) in the context of Bayesian uncertainty quantification in parallel...
Stephen Wu +3 more
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Markov chains and embedded Markov chains in geology
Journal of the International Association for Mathematical Geology, 1969Geological data are structured as first-order, discrete-state discrete-time Markov chains in two main ways. In one, observations are spaced equally in time or space to yield transition probability matrices with nonzero elements in the main diagonal; in the other, only state transitions are recorded, to yield matrices with diagonal elements exactly ...
W. C. Krumbein, Michael F. Dacey
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1993
Publisher Summary This chapter focuses on Markov chains of the probability theory. It presents a stochastic process {Xn, n = 0, 1, 2…} that takes on a finite or countable number of possible values. This set of possible values of the process is denoted by the set of nonnegative integers {0, 1, 2…}.
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Publisher Summary This chapter focuses on Markov chains of the probability theory. It presents a stochastic process {Xn, n = 0, 1, 2…} that takes on a finite or countable number of possible values. This set of possible values of the process is denoted by the set of nonnegative integers {0, 1, 2…}.
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2005
Abstract Useful models of the real world have to satisfy two conflicting requirements: they must be sufficiently complicated to describe complex systems, but they must also be sufficiently simple for us to analyse them. This chapter introduces Markov chains, which have successfully modelled a huge range of scientific and social phenomena,
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Abstract Useful models of the real world have to satisfy two conflicting requirements: they must be sufficiently complicated to describe complex systems, but they must also be sufficiently simple for us to analyse them. This chapter introduces Markov chains, which have successfully modelled a huge range of scientific and social phenomena,
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Bayesian computation via the gibbs sampler and related markov chain monte carlo methods (with discus
, 1993The use of the Gibbs sampler for Bayesian computation is reviewed and illustrated in the context of some canonical examples. Other Markov chain Monte Carlo simulation methods are also briefly described, and comments are made on the advantages of sample ...
Adrian F. M. Smith, G. Roberts
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Nature Methods, 2019
You can look back there to explain things, but the explanation disappears. You’ll never find it there. Things are not explained by the past. They’re explained by what happens now.
Naomi Altman +2 more
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You can look back there to explain things, but the explanation disappears. You’ll never find it there. Things are not explained by the past. They’re explained by what happens now.
Naomi Altman +2 more
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