Monte Carlo samplingLatin hypercube samplingTransformation law of probability densityUncertainty propagationIn engineering, uncertainties pervade product lifecycles, presenting significant challenges to design
Additionally, stochastic factors exist simultaneously with impacts and make the nonlinear dynamics more complicated. There are many efforts in this field. As commonly known, the response of the stochastic system will be a stochastic process and its probability density function is governed by a correspo...
monte-carloprobabilitystochasticmonte-carlo-simulationstochastic-processuncertainty-quantificationprobabilisticuncertainty-propagationlatin-hypercubeuncertainty-samplinglatin-hypercube-sampling UpdatedApr 1, 2025 Python openturns/openturns Star266 Probabilistic modelling and uncertainty quantification library ...
We also prove exponential convergence of the probability of a large deviation for the optimal value of the SAA, the true value of an optimal solution of the SAA, and the probability that any optimal solution to the SAA is an optimal solution of the true problem. All of these results can ...
state-of-the-art quantum algorithms for quantum machine learning avoid the expectation-value estimation by solving sampling problems so that the speedup should not be canceled out94,95,96, and further research is needed to clarify how we can similarly avoid the expectation-value estimation in quant...
We stress that the generated sequence is a stochastic process which updates iteratively according to the chosen projection algorithm and the sampling information used in each iteration. Therefore, asymptotic convergence of the stochastic approximation method guarantees a solution with total probability. The...
Finally, we discuss the Monte Carlo sample average approach to solving such min-max problems. Key words: stochastic programming, min-max optimization, problem of moments, Monte Carlo sampling, sample average approximation. # Supported, in part, by the National Science Foundation und... 展开 ...
Stochastic Simulation : Algorithms and Analysis (Stochastic Modelling and Applied Probability) (No. 100) Søren Asmussen、Peter W. Glynn / Springer / 2007-07-27 / USD 64.95 (少于10人评价) Sampling-based computational methods have become a fundamental part of the num... Statistics of Ran...
Among the most used MPS simulation algorithms that model non-stationarity, we list Sequential Normal Equation Simulation (SNESIM) (Strebelle, 2002), Direct Sampling (DS) (Mariethoz et al., 2010) and Cross-correlation Simulation (CCSIM) (Tahmasebi et al., 2012). In SNESIM, probability maps ...
The structured sampling techniques, such as stratified sampling and Latin hypercube sampling, can be used to further improve the computational efficacy. In stratified sampling, the sample space is participated into a number of strata or subgroups, with each stratum having a specified probability of ...