Results 291 to 300 of about 144,876 (334)

Generators of Markov Chains

2020
Elementary treatments of Markov chains, especially those devoted to discrete-time and finite state-space theory, leave the impression that everything is smooth and easy to understand. This exposition of the works of Kolmogorov, Feller, Chung, Kato, and other mathematical luminaries, which focuses on time-continuous chains but is not so far from being ...
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Generalized Normalizing Flows via Markov Chains

2023
Normalizing flows, diffusion normalizing flows and variational autoencoders are powerful generative models. This Element provides a unified framework to handle these approaches via Markov chains. The authors consider stochastic normalizing flows as a pair of Markov chains fulfilling some properties, and show how many state-of-the-art models for data ...
Paul Lyonel Hagemann, Johannes Hertrich, Gabriele Steidl   +2 more
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Martingale generating functions for Markov chains

Journal of Statistical Planning and Inference, 2002
Let \(X=(X(t))_{t\in T}\) be a Markov chain with either discrete \([T=\{0,1,2, \dots,\}]\) or continuous parameter \((T=[0,\infty))\). The authors are concerned with martingales of the form \(\psi(X)\) or \(\varphi(X,\tau)\), where \(\tau\) is a stopping time for \(X\).
Williams, E. J., Watson, R. K.
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Generating Markov-Chain Transitions Quickly: I

ORSA Journal on Computing, 1990
We give a way to generate transitions directly from a compact representation of the generator of a continuous-time Markov chain corresponding to a class that includes many queueing networks and reliability problems. Under specified conditions, this is (provably) faster than generating these transitions via a future-event schedule.
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The general markov chain disorder problem

Stochastics, 1987
This paper treats a special case of the disorder problem. It is assumed that the data consist of noisy observations of the state of a Markov chain, and that at some unknown time S a sudden change occurs, either in the state transition probabilities or in the observation process. The goal is to find a Markov time x which minimizes E[C{t - S), where C{n}
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Patterns generated by -order Markov chains

Statistics & Probability Letters, 2010
We derive an expression for the expected time for a pattern to appear in higher-order Markov chains with and without a starting sequence. This yields a result for directly calculating, the first time one of a collection of patterns appears, in addition to the probability, for each pattern, that it is the first to appear.
Evan Fisher, Shiliang Cui
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Criteria for classifying general Markov chains

Advances in Applied Probability, 1976
The aim of this paper is to present a comprehensive set of criteria for classifying as recurrent, transient, null or positive the sets visited by a general state space Markov chain. When the chain is irreducible in some sense, these then provide criteria for classifying the chain itself, provided the sets considered actually reflect the status of the ...
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A Markov Chain Sequence Generator for Power Macromodeling

IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems, 2002
In this paper, we present a novel sequence generator based on a Markov chain model. Specifically, we formulate the problem of generating a sequence of vectors with given average input probability p, average transition density d, and spatial correlation s as a transition matrix computation problem, in which the matrix elements are subject to constraints
null Xun Liu, M.C. Papaefthymiou
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Markov chains and generalized continued fractions

Journal of Applied Probability, 1992
This paper deals with a class of discrete-time Markov chains for which the invariant measures can be expressed in terms of generalized continued fractions. The representation covers a wide class of stochastic models and is well suited for numerical applications. The results obtained can easily be extended to continuous-time Markov chains.
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Limit Theorems for General Markov Chains

Siberian Mathematical Journal, 2001
Let S be a measurable space with a σ-algebra B(S) of measurable sets. Let Pn(x,B), x ∈ S, B ∈ B(S), be some transition probability on S; in this article, the parameter n, time, ranges in the set Z+ = {0, 1, 2, . . . } of nonnegative integers. The transition probability is not assumed homogeneous in time n.
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