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510,405
Quantitative model checking of continuoustime Markov chains against timed automata specification
, 2009
"... We study the following problem: given a continuoustime Markov chain (CTMC) C, and a linear realtime property provided as a deterministic timed automaton (DTA) A, what is the probability of the set of paths of C that are accepted by A (C satisfies A)? It is shown that this set of paths is measurabl ..."
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Cited by 23 (6 self)
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We study the following problem: given a continuoustime Markov chain (CTMC) C, and a linear realtime property provided as a deterministic timed automaton (DTA) A, what is the probability of the set of paths of C that are accepted by A (C satisfies A)? It is shown that this set of paths
MODEL CHECKING OF CONTINUOUSTIME MARKOV CHAINS AGAINST TIMED AUTOMATA SPECIFICATIONS
, 2009
"... Vol. 7 (1:12) 2011, pp. 1–34 www.lmcsonline.org ..."
The Theory of Hybrid Automata
, 1996
"... A hybrid automaton is a formal model for a mixed discretecontinuous system. We classify hybrid automata acoording to what questions about their behavior can be answered algorithmically. The classification reveals structure on mixed discretecontinuous state spaces that was previously studied on pur ..."
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Cited by 680 (13 self)
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A hybrid automaton is a formal model for a mixed discretecontinuous system. We classify hybrid automata acoording to what questions about their behavior can be answered algorithmically. The classification reveals structure on mixed discretecontinuous state spaces that was previously studied
Exact Sampling with Coupled Markov Chains and Applications to Statistical Mechanics
, 1996
"... For many applications it is useful to sample from a finite set of objects in accordance with some particular distribution. One approach is to run an ergodic (i.e., irreducible aperiodic) Markov chain whose stationary distribution is the desired distribution on this set; after the Markov chain has ..."
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Cited by 548 (13 self)
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For many applications it is useful to sample from a finite set of objects in accordance with some particular distribution. One approach is to run an ergodic (i.e., irreducible aperiodic) Markov chain whose stationary distribution is the desired distribution on this set; after the Markov chain
Approximate symbolic model checking of continuoustime Markov chains (Extended Abstract)
, 1999
"... . This paper presents a symbolic model checking algorithm for continuoustime Markov chains for an extension of the continuous stochastic logic CSL of Aziz et al [1]. The considered logic contains a timebounded untiloperator and a novel operator to express steadystate probabilities. We show that t ..."
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Cited by 156 (25 self)
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. This paper presents a symbolic model checking algorithm for continuoustime Markov chains for an extension of the continuous stochastic logic CSL of Aziz et al [1]. The considered logic contains a timebounded untiloperator and a novel operator to express steadystate probabilities. We show
ModelChecking in Dense Realtime
 INFORMATION AND COMPUTATION
, 1993
"... Modelchecking is a method of verifying concurrent systems in which a statetransition graph model of the system behavior is compared with a temporal logic formula. This paper extends modelchecking for the branchingtime logic CTL to the analysis of realtime systems, whose correctness depends on t ..."
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Cited by 329 (7 self)
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on the magnitudes of the timing delays. For specifications, we extend the syntax of CTL to allow quantitative temporal operators such as 93!5 , meaning "possibly within 5 time units." The formulas of the resulting logic, Timed CTL (TCTL), are interpreted over continuous computation trees, trees in which
Finite state Markovchain approximations to univariate and vector autoregressions
 Economics Letters
, 1986
"... The paper develops a procedure for finding a discretevalued Markov chain whose sample paths approximate well those of a vector autoregression. The procedure has applications in those areas of economics, finance, and econometrics where approximate solutions to integral equations are required. 1. ..."
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Cited by 472 (0 self)
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The paper develops a procedure for finding a discretevalued Markov chain whose sample paths approximate well those of a vector autoregression. The procedure has applications in those areas of economics, finance, and econometrics where approximate solutions to integral equations are required. 1.
Segmentation of brain MR images through a hidden Markov random field model and the expectationmaximization algorithm
 IEEE TRANSACTIONS ON MEDICAL. IMAGING
, 2001
"... The finite mixture (FM) model is the most commonly used model for statistical segmentation of brain magnetic resonance (MR) images because of its simple mathematical form and the piecewise constant nature of ideal brain MR images. However, being a histogrambased model, the FM has an intrinsic limi ..."
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Cited by 619 (14 self)
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based methods produce unreliable results. In this paper, we propose a novel hidden Markov random field (HMRF) model, which is a stochastic process generated by a MRF whose state sequence cannot be observed directly but which can be indirectly estimated through observations. Mathematically, it can be shown
Predicting Transmembrane Protein Topology with a Hidden Markov Model: Application to Complete Genomes
 J. MOL. BIOL
, 2001
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Results 1  10
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