Results 101 to 110 of about 771,482 (302)
Modelling the profitability of credit cards by Markov decision processes
, 2009 This paper derives a model for the profitability of credit cards, which allow lenders to find the optimal dynamic credit limit policy. The model is a Markov decision process, where the states of the system are based on the borrower's behavioural score ...
Thomas, Lyn C., So, Mee Chi
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Publications de l'Institut Mathematique, 2008 Let {Xi, i _? 1} denote a sequence of variables that take values in {0, 1} and suppose that the sequence forms a Markov chain with transition matrix P and with initial distribution (q, p) = (P(X1 = 0), P(X1 = 1)). Several authors have studied the quantities Sn, Y (r) and AR(n), where Sn = ?n i=1 Xi denotes the number of successes, where Y (r) denotes ...
Omey, Edward, Van Gulck, Stefan
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Accelerating Materials Discovery: A Review of Machine Learning in X‐Ray Absorption Spectroscopy
Advanced Intelligent Systems, EarlyView.This review systematically details how machine learning transforms X‐ray absorption spectroscopy (XAS) analysis. It covers advanced deep learning architectures for structure‐spectra mapping and inverse tasks, while discussing key challenges like the simulation‐to‐reality gap.
Melaku Lake Tegegne +5 more
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American Journal of Botany, EarlyView.Abstract Premise Desert plant assemblages in southern California provide an opportunity to link patterns of community structure with climate‐driven vulnerability in a rapidly changing environment. California sustains an exceptionally diverse flora of approximately 4300 plant species, with 31% identified as endemic.
Hector Zumbado‐Ulate +4 more
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Non-homogeneous Markov chains with a finite state space and a Doeblin type theorem
International Journal of Mathematics and Mathematical Sciences, 1995 Doeblin [1] considered some classes of finite state nonhomogeneous Markov chains and studied their asymptotic behavior. Later Cohn [2] considered another class of such Markov chains (not covered earlier) and obtained Doeblin type results.
Rita Chattopadhyay
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Journal of Statistical Software, 2016 Markov chains are well-established probabilistic models of a wide variety of real systems that evolve along time. Countless examples of applications of Markov chains that successfully capture the probabilistic nature of real problems include areas as ...
Pablo J. Villacorta, José L. Verdegay
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American Journal of Botany, EarlyView.Abstract Premise The modern distribution of the southern cypresses, Callitroideae (Cupressaceae), reflects a classic Gondwanan distribution. The initial diversification of Callitroideae is estimated to have occurred during the Cretaceous, yet fossils from this period are rare, hampering our understanding of its early evolutionary history.
Kelly C. Pfeiler +5 more
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American Journal of Community Psychology, EarlyView.Abstract Mixed evidence for the influence of structural and social factors on adolescent substance use behaviors exists across the rural–urban continuum. Therefore, this study explores how adolescent perceptions of structural and social community risk factors are associated with lifetime and past 30‐day use of alcohol, marijuana, cigarettes, and ...
Melissa Pearman Fenton +4 more
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On a Class of Markov Chains [PDF]
Journal of the Australian Mathematical Society, 1971 Until recently, very little work has been done on the second order properties of Markov chains. Craven [1] has studied the joint distributions of Markov chains having a Borel subset of n-dimensional Euclidean space as state space. His idea was to consider the process as a time series.
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Equivalent Conditions for Irreducibility of Discrete Time Markov Chains [PDF]
We consider discrete time Markov chains on general state space. It is shown that a certain property referred to here as nondecomposability is equivalent to irreducibility, and that a Markov chain with invariant distribution is irreducible if and only if ...
Cuong Le Van, John Stachurski
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