Results 31 to 40 of about 1,128,212 (150)
Applications of Little's Law to stochastic models of gene expression
, 2010 The intrinsic stochasticity of gene expression can lead to large variations in protein levels across a population of cells. To explain this variability, different sources of mRNA fluctuations ('Poisson' and 'Telegraph' processes) have been proposed in ...
Elgart, Vlad +2 more
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Construction and Verification of Performance and Reliability Models [PDF]
, 2001 Over the last two decades formal methods have been extended towards performance and reliability evaluation. This paper tries to provide a rather intuitive explanation of the basic concepts and features in this area. Instead of striving for mathematical
Hermanns, H.
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Precise asymptotics: robust stochastic volatility models
, 2018 We present a new methodology to analyze large classes of (classical and rough) stochastic volatility models, with special regard to short-time and small noise formulae for option prices.
Friz, Peter K. +2 more
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Abstractions of Stochastic Hybrid Systems [PDF]
, 2008 In this paper we define a stochastic bisimulation concept for a very general class of stochastic hybrid systems, which subsumes most classes of stochastic hybrid systems.
Bujorianu, L.M. +2 more
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Stochastic Collapsed Variational Inference for Sequential Data
, 2015 Stochastic variational inference for collapsed models has recently been successfully applied to large scale topic modelling. In this paper, we propose a stochastic collapsed variational inference algorithm in the sequential data setting. Our algorithm is
Blunsom, Phil, Wang, Pengyu
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A Neural Stochastic Volatility Model
, 2018 In this paper, we show that the recent integration of statistical models with deep recurrent neural networks provides a new way of formulating volatility (the degree of variation of time series) models that have been widely used in time series analysis ...
Luo, Rui +3 more
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Markov Chains Approximations of jump-Diffusion Quantum Trajectories
, 2008 "Quantum trajectories" are solutions of stochastic differential equations also called Belavkin or Stochastic Schr\"odinger Equations. They describe random phenomena in quantum measurement theory. Two types of such equations are usually considered, one is
Pellegrini, Clement
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Simulation of a particle-laden turbulent channel flow using an improved stochastic Lagrangian model
, 2009 The purpose of this paper is to examine the Lagrangian stochastic modeling of the fluid velocity seen by inertial particles in a nonhomogeneous turbulent flow. A new Langevin-type model, compatible with the transport equation of the drift velocity in the
Anne Tanière +5 more
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A comparison of numerical approaches for statistical inference with stochastic models. [PDF]
Stoch Environ Res Risk Assess, 2023 Bacci M +4 more
europepmc +1 more source
Stochastic frontier models: a bayesian perspective [PDF]
, 1992 A Bayesian approach to estimation, prediction and model comparison in composed error production models is presented. A broad range of distributions on the inefficiency term define the contending models, which can either be treated separately or pooled ...
Broeck, Julien Van den +3 more
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