The Experts below are selected from a list of 85005 Experts worldwide ranked by ideXlab platform
Jeanlouis Bon - One of the best experts on this subject based on the ideXlab platform.
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a new formalism that combines advantages of fault trees and Markov models boolean logic driven Markov Processes
Reliability Engineering & System Safety, 2003Co-Authors: Marc Bouissou, Jeanlouis BonAbstract:Abstract This paper introduces a modeling formalism that enables the analyst to combine concepts inherited from fault trees and Markov models in a new way. We call this formalism Boolean logic Driven Markov Processes (BDMP). It has two advantages over conventional models used in dependability assessment: it allows the definition of complex dynamic models while remaining nearly as readable and easy to build as fault-trees, and it offers interesting mathematical properties, which enable an efficient processing for BDMP that are equivalent to Markov Processes with huge state spaces. We give a mathematical definition of BDMP, the demonstration of their properties, and several examples to illustrate how powerful and easy to use they are. From a mathematical point of view, a BDMP is nothing more than a certain way to define a global Markov process, as the result of several elementary Processes which can interact in a given manner. An extreme case is when the Processes are independent. Then we simply have a fault-tree, the leaves of which are associated to independent Markov Processes.
Jeanjacques Lesage - One of the best experts on this subject based on the ideXlab platform.
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generalized boolean logic driven Markov Processes a powerful modeling framework for model based safety analysis of dynamic repairable and reconfigurable systems
Reliability Engineering & System Safety, 2017Co-Authors: Pierreyves Piriou, Jeanmarc Faure, Jeanjacques LesageAbstract:This paper presents a modeling framework that permits to describe in an integrated manner the structure of the critical system to analyze, by using an enriched fault tree, the dysfunctional behavior of its components, by means of Markov Processes, and the reconfiguration strategies that have been planned to ensure safety and availability, with Moore machines. This framework has been developed from BDMP (Boolean logic Driven Markov Processes), a previous framework for dynamic repairable systems. First, the contribution is motivated by pinpointing the limitations of BDMP to model complex reconfiguration strategies and the failures of the control of these strategies. The syntax and semantics of GBDMP (Generalized Boolean logic Driven Markov Processes) are then formally defined; in particular, an algorithm to analyze the dynamic behavior of a GBDMP model is developed. The modeling capabilities of this framework are illustrated on three representative examples. Last, qualitative and quantitative analysis of GDBMP models highlight the benefits of the approach.
Steven I. Marcus - One of the best experts on this subject based on the ideXlab platform.
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risk sensitive control of Markov Processes in countable state space
Systems & Control Letters, 1996Co-Authors: Daniel Hernandezhernandez, Steven I. MarcusAbstract:In this paper we consider infinite horizon risk-sensitive control of Markov Processes with discrete time and denumerable state space. This problem is solved by proving, under suitable conditions, that there exists a bounded solution to the dynamic programming equation. The dynamic programming equation is transformed into an Isaacs equation for a stochastic game, and the vanishing discount method is used to study its solution. In addition, we prove that the existence conditions are also necessary.
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Discrete-time controlled Markov Processes with average cost criterion: a survey
SIAM Journal on Control and Optimization, 1993Co-Authors: Aristotle Arapostathis, Emmanuel Fernández-gaucherand, Vivek S. Borkar, Mrinal K. Ghosh, Steven I. MarcusAbstract:This work is a survey of the average cost control problem for discrete-time Markov Processes. The authors have attempted to put together a comprehensive account of the considerable research on this problem over the past three decades. The exposition ranges from finite to Borel state and action spaces and includes a variety of methodologies to find and characterize optimal policies. The authors have included a brief historical perspective of the research efforts in this area and have compiled a substantial yet not exhaustive bibliography. The authors have also identified several important questions that are still open to investigation.
Pierreyves Piriou - One of the best experts on this subject based on the ideXlab platform.
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generalized boolean logic driven Markov Processes a powerful modeling framework for model based safety analysis of dynamic repairable and reconfigurable systems
Reliability Engineering & System Safety, 2017Co-Authors: Pierreyves Piriou, Jeanmarc Faure, Jeanjacques LesageAbstract:This paper presents a modeling framework that permits to describe in an integrated manner the structure of the critical system to analyze, by using an enriched fault tree, the dysfunctional behavior of its components, by means of Markov Processes, and the reconfiguration strategies that have been planned to ensure safety and availability, with Moore machines. This framework has been developed from BDMP (Boolean logic Driven Markov Processes), a previous framework for dynamic repairable systems. First, the contribution is motivated by pinpointing the limitations of BDMP to model complex reconfiguration strategies and the failures of the control of these strategies. The syntax and semantics of GBDMP (Generalized Boolean logic Driven Markov Processes) are then formally defined; in particular, an algorithm to analyze the dynamic behavior of a GBDMP model is developed. The modeling capabilities of this framework are illustrated on three representative examples. Last, qualitative and quantitative analysis of GDBMP models highlight the benefits of the approach.
B Prakasa L S Rao - One of the best experts on this subject based on the ideXlab platform.
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moderate deviation principle for maximum likelihood estimator for Markov Processes
Statistics & Probability Letters, 2018Co-Authors: B Prakasa L S RaoAbstract:Abstract After a short review of the properties of the maximum likelihood estimator for discrete time Markov Processes, we obtain a moderate deviation result for such an estimator under some regularity conditions using the Gartner–Ellis theorem for random Processes.
