Results 1 - 10 of 222

It Usually Works: The Temporal Logic of Stochastic Systems

by Adnan Aziz, Vigyan Singhal, Felice Balarin, Robert K. Brayton, Alberto L. Sangiovanni-vincentelli , 1995
"... . In this paper the branching time logic pCTL is defined. pCTL expresses quantitative bounds on the probabilities of correct behavior; it can be interpreted over discrete Markov processes. A bisimulation relation is defined on finite Markov processes, and shown to be sound and complete with re ..."
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with respect to pCTL . We extend the universe of models to generalized Markov processes in order to support notions of refinement, abstraction, and parametrization. Model checking pCTL over generalized Markov processes is shown to be elementary by a reduction to RCF. We conclude by describing practical

Quantum stochastic processes as models for state vector reduction

by L Didsi , 1987
"... Abstract. An elementary introduction of quantum-state-valued Markovian stochastic pro-cesses (QSP) for N-state quantum systems is given. It is pointed out that a so-called master constraint must be fulfilled. For a given master equation a continuous and, as a new alternative possibility, a discontin ..."
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Abstract. An elementary introduction of quantum-state-valued Markovian stochastic pro-cesses (QSP) for N-state quantum systems is given. It is pointed out that a so-called master constraint must be fulfilled. For a given master equation a continuous and, as a new alternative possibility, a

Abstract Modelling and Experimental Results on Stochastic Model Reduction, Protein Maturation,

by Guangqiang Dong, Macromolecular Crowding, Time-varying Gene Expression, Guangqiang Dong , 2009
"... Gene expression, which connects genomic information to functional units in living cells, has received substantial attention since the completion of The Human Genome Project. Quantita-tive characterization of gene expression will provide valuable information for understanding the behavior of living c ..."
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cells, and possibilities of building synthetic gene circuits to control or modify the behavior of naturally occurring cells. Many aspects of quantitative gene expression have been studied, including gene expression dynamics and noise in E. coli. The gene expression process itself is stochastic

Model reduction for a class of singularly perturbed stochastic differential equations

by Narmada Herath , Abdullah Hamadeh , Domitilla Del Vecchio
"... Abstract-A class of singularly perturbed stochastic differential equations (SDE) with linear drift and nonlinear diffusion terms is considered. We prove that, on a finite time interval, the trajectories of the slow variables can be well approximated by those of a system with reduced dimension as th ..."
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Abstract-A class of singularly perturbed stochastic differential equations (SDE) with linear drift and nonlinear diffusion terms is considered. We prove that, on a finite time interval, the trajectories of the slow variables can be well approximated by those of a system with reduced dimension

Burstiness Reduction of a Doubly Stochastic AR-Modeled Uniform Activity

by J. P. Dubois - VBR Video, Proc. WASET
"... Abstract—Stochastic modeling of network traffic is an area of significant research activity for current and future broadband communication networks. Multimedia traffic is statistically characterized by a bursty variable bit rate (VBR) profile. In this paper, we develop an improved model for uniform ..."
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Abstract—Stochastic modeling of network traffic is an area of significant research activity for current and future broadband communication networks. Multimedia traffic is statistically characterized by a bursty variable bit rate (VBR) profile. In this paper, we develop an improved model for uniform

Gaussian processes autoencoder for dimensionality reduction

by Gao X Jiang , J , Hong , X , Cai
"... Abstract. Learning low dimensional manifold from highly nonlinear data of high dimensionality has become increasingly important for discovering intrinsic representation that can be utilized for data visualization and preprocessing. The autoencoder is a powerful dimensionality reduction technique ba ..."
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Abstract. Learning low dimensional manifold from highly nonlinear data of high dimensionality has become increasingly important for discovering intrinsic representation that can be utilized for data visualization and preprocessing. The autoencoder is a powerful dimensionality reduction technique

STOCHASTIC REDUCTION METHOD FOR BIOLOGICAL CHEMICAL KINETICS USING TIME-SCALE SEPARATION

by Chetan D. Pahlajani, Paul J. Atzberger, Mustafa Khammash
"... Abstract. Many processes in cell biology encode and process information and enact responses by modulating the concentrations of biological molecules. Such modulations serve functions ranging from encoding and transmitting information about external stimuli to regulating internal metabolic states. To ..."
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Abstract. Many processes in cell biology encode and process information and enact responses by modulating the concentrations of biological molecules. Such modulations serve functions ranging from encoding and transmitting information about external stimuli to regulating internal metabolic states

Scenario reduction and scenario tree construction for power management problems

by Nicole Gröwe-kuska, Holger Heitsch, Werner Römisch - Power Management Problems, IEEE Bologna Power Tech Proceedings , 2003
"... Abstract — Portfolio and risk management problems of power utilities may be modeled by multistage stochastic programs. These models use a set of scenarios and corresponding probabilities to model the multivariate random data process (electrical load, stream flows to hydro units, and fuel and electri ..."
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Abstract — Portfolio and risk management problems of power utilities may be modeled by multistage stochastic programs. These models use a set of scenarios and corresponding probabilities to model the multivariate random data process (electrical load, stream flows to hydro units, and fuel

Modeling of a Highly Nonlinear Stochastic Process by Neural Networks

by Pero Radonja, Srdjan Stankovic
"... Abstract- Methods of identifying of highly nonlinear processes defined by a small data set are presented in this paper. Neural networks of different structures are implemented on two types of data set in order to get corresponding nonlinear models. Two layers NN based on Levenberg-Marquardt algorith ..."
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Abstract- Methods of identifying of highly nonlinear processes defined by a small data set are presented in this paper. Neural networks of different structures are implemented on two types of data set in order to get corresponding nonlinear models. Two layers NN based on Levenberg

Error reduction and convergence in climate prediction

by Charles S Jackson , Mrinal K Sen , Gabriel Huerta , Y I Deng , Kenneth P Bowman - Journal of Climate , 2008
"... ABSTRACT Although climate models have steadily improved their ability to reproduce the observed climate, over the years there has been little change to the wide range of sensitivities exhibited by different models to a doubling of atmospheric CO 2 concentrations. Stochastic optimization is used to ..."
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ABSTRACT Although climate models have steadily improved their ability to reproduce the observed climate, over the years there has been little change to the wide range of sensitivities exhibited by different models to a doubling of atmospheric CO 2 concentrations. Stochastic optimization is used
Results 1 - 10 of 222