Results 231 to 240 of about 164,916 (266)
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2019
The classical model of a real-time system consists of a number of tasks, each of which has an execution time which is upper bounded by a constant, referred to as the worst-case execution time (WCET). Further, jobs of each task execute periodically or sporadically, subject to some minimum inter-arrival time.
Maxim, Dorin +2 more
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The classical model of a real-time system consists of a number of tasks, each of which has an execution time which is upper bounded by a constant, referred to as the worst-case execution time (WCET). Further, jobs of each task execute periodically or sporadically, subject to some minimum inter-arrival time.
Maxim, Dorin +2 more
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Statistic Analysis for Probabilistic Processes
2009 24th Annual IEEE Symposium on Logic In Computer Science, 2009We associate a statistical vector to a trace and a geometrical embedding to a Markov Decision Process, based on a distance on words, and study basic Membership and Equivalence problems. The Membership problem for a trace \textit{w} and a Markov Decision Process \textit{S} decides if there exists a strategy on \textit{S} which generates with high ...
Michel de Rougemont, Mathieu Tracol
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A probabilistic analysis of loop programs
Computer Languages, 1989The paper initiates a probabilistic analysis of loop programs that is based on the discovery that the Meyer-Ritchie upper bounds on the running times of loop programs are restrictions of probabilistic properties of stochastic networks. The idea of this probabilistic approach to the complexity problem of loop programs is that flow-graphs of loop ...
Manfred E. Szabo, E. J. Farkas
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Probabilistic analysis of CAN with faults
23rd IEEE Real-Time Systems Symposium, 2002. RTSS 2002., 2003As CANs (controller area networks) are being increasingly used in safety-critical applications, there is a need for accurate predictions of failure probability. In this paper we provide a general probabilistic schedulability analysis technique which is applied specifically to CANs to determine the effect of random network faults on the response times ...
Ian Broster +2 more
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Probabilistic analysis of adaptative sampling [PDF]
Random Structures and Algorithms, 1997 Summary: This paper analyzes the asymptotic properties of a classical algorithm: the adaptative sampling which solves the following problem; how to estimate the number \(M\) of distinct elements of a large collection of \(n\) data. Using tools such as the random tree and techniques such as Mellin transforms, combinatorial identities on Stirling numbers
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Probabilistic, Multidimensional Unfolding Analysis
Psychometrika, 1974A probabilistic, multidimensional version of Coombs' unfolding model is obtained by assuming that the projections of each stimulus and each individual on each axis are normally distributed. Exact equations are developed for the single dimensional case and an approximate one for the multidimensional case.
Zinnes, Joseph L., Griggs, Richard A.
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A Probabilistic Framework for Schedulability Analysis
2003The limitations of the deterministic formulation of scheduling are outlined and a probabilistic approach is motivated. A number of models are reviewed with one being chosen as a basic framework. Response-time analysis is extended to incorporate a probabilistic characterisation of task arrivals and execution times.
Alan Burns 0001 +2 more
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2015
This chapter provides the motivations behind the usage of probabilistic analysis in the domains of science and engineering. This is followed by a brief introduction of some of the foremost concepts of probabilistic analysis and the widely used probabilistic analysis techniques.
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This chapter provides the motivations behind the usage of probabilistic analysis in the domains of science and engineering. This is followed by a brief introduction of some of the foremost concepts of probabilistic analysis and the widely used probabilistic analysis techniques.
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Probabilistic Semantic Analysis of Speech
1997This paper presents a new probabilistic approach to semantic analysis of speech. The problem of finding the semantic contents of a word chain is modeled as the problem of assigning semantic attributes to words. The discrete assignment function is characterized by random vectors and its probabilities.
Jürgen Haas +3 more
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DeepAR: Probabilistic forecasting with autoregressive recurrent networks
International Journal of Forecasting, 2020David Salinas +2 more
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