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General transition probabilities for finite Markov chains
Mathematical Proceedings of the Cambridge Philosophical Society, 1964We consider a stationary discrete-time Markov chain with a finite number m of possible states which we designate by 1,…,m. We assume that at time t = 0 the process is in an initial state i with probability (i = 1,…, m) and such that and .
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A Markov chains prognostics framework for complex degradation processes
Reliability Engineering and System Safety, 2020Juan Chiachío +2 more
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Value Convergence in Generalized Markov Decision Chains
1994We consider a generalized Markov decision chain with state space S = {1,..., m}, action set ∆ i in i ∈ S (∆ i is finite, nonempty) and the set of policies ∆ = X i ∈ S ∆ i . To each δ ∈ ∆, there exists an m × m “transition matrix” P δ and an m by one real vector c δ . We only suppose that P δ is nonnegative, row sums can be greater than one.
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The Markov chain interest rate scenario generator revisited
1994Summary: This paper furthers the development of the Markov chain interest rate generator. Though the basic technique remains essentially unchanged, there are still many significant changes to the model. For example: (i) the long (key) rates are now are generated by a mean reversionary process; (ii) the number of shapes is increased from seven to 11 ...
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Building ubiquitin chains: E2 enzymes at work
Nature Reviews Molecular Cell Biology, 2009Yihong Ye, Michael Rape
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Fuzzy Markov Random Fields versus Chains for Multispectral Image Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence, 2006Christophe Collet
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Switching Markov chains for a holistic modeling of SIS unavailability
Reliability Engineering and System Safety, 2015Walid Mechri, Christophe Simon
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