Stochastic simulation algorithm

his fast forward selection algorithm is needed to cut down the number of samples while keeping most of the stochastic information embedded in such samples. The LHS-FFS-based P-LMP is investigated using IEEE 6-bus and 24-bus systems. This method is compared with SRS and LHS only. The LHS-FFS approach is found to be efficient and flexible; therefore, it has the potential to be applied in many power system probabilistic problems.

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SgnesR (Stochastic Gene Network Expression Simulator in R) is an R package that provides an interface to simulate gene expression data from a given gene network using the stochastic simulation algorithm (SSA).

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An optimization-simulation strategy has been applied by coupling a commercial process simulator (Aspen HYSYS ) with a programming tool (MATLAB ) to produce a precise steady state simulationbased optimization of a whole green-field saturated gas plant as a real case study. The plant has more than 100-components and comprises interacting three-phase fractionation towers, pumps, compressors and exchangers. The literature predominantly uses this coupling to optimize individual units at small scales, while paying more attention to optimizing discrete design decisions.

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This book presents and develops major numerical methods currently used for solving problems arising in quantitative finance. Our presentation splits into two parts. Part I is methodological, and offers a comprehensive toolkit on numerical methods and algorithms. This includes Monte Carlo simulation, numerical schemes for partial differential equations, stochastic optimization in discrete time, copula functions, transform-based methods and quadrature techniques. Part II is practical, and features a number of self-contained cases.

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Stochastic Optimality Theory (Boersma, 1997) is a widely-used model in linguistics that did not have a theoretically sound learning method previously. In this paper, a Markov chain Monte-Carlo method is proposed for learning Stochastic OT Grammars. Following a Bayesian framework, the goal is finding the posterior distribution of the grammar given the relative frequencies of input-output pairs. The Data Augmentation algorithm allows one to simulate a joint posterior distribution by iterating two conditional sampling steps. ...

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Tuyển tập các báo cáo nghiên cứu về y học được đăng trên tạp chí y học Critical Care giúp cho các bạn có thêm kiến thức về ngành y học đề tài: Diagnostic accuracy of pattern differentiation algorithm based on Chinese medicine theory: a stochastic simulation study...

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Tuyển tập các báo cáo nghiên cứu về y học được đăng trên tạp chí y học Critical Care giúp cho các bạn có thêm kiến thức về ngành y học đề tài: Misdiagnosis and undiagnosis due to pattern similarity in Chinese medicine: a stochastic simulation study using pattern differentiation algorithm...

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Web search engine: Markov chain theory Data Mining, Machine Learning: Data mining, Machine learning: Stochastic gradient, Markov chain Monte Carlo, Image processing: Markov random fields, Design of wireless communication systems: random matrix theory, Optimization of engineering processes: simulated annealing, genetic algorithms, Finance (option pricing, volatility models): Monte Carlo, dynamic models, Design of atomic bomb (Los Alamos): Markov chain Monte Carlo.

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Algorithms for Non-Product-Form Networks Although many algorithms are available for solving product-form queueing networks (see Chapters 8 and 9), most practical queueing problems lead to non-product-form networks. If the network is Markovian (or can be Markovized), automated generation and solution of the underlying CTMC via stochastic Petri nets (SPNs) is an option provided the number of states is fewer than a million. Instead of the costly alternative of a discrete-event simulation, approximate solution may be considered.

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