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Monte Carlo sampling techniques

Coupling Importance Sampling and Multilevel Monte Carlo using Sample Average Approximation

Coupling Importance Sampling and Multilevel Monte Carlo using Sample Average Approximation

... bins Monro algorithm proposed by Chen et al. [7, 8] and later investigated in the context of importance sampling by Lapeyre and Lelong [25] and Lelong [26]. The numerical stability of these stochastic algorithms ...

34

Importance Sampling-Based Monte Carlo Methods with Applications to Quantitative Finance

Importance Sampling-Based Monte Carlo Methods with Applications to Quantitative Finance

... quasi-Monte Carlo techniques have great potential to increase the efficiency of sequential Monte Carlo ...importance sampling in the marginal space of the ...quasi-Monte ...

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Optimised Importance Sampling in Multilevel Monte Carlo

Optimised Importance Sampling in Multilevel Monte Carlo

... two techniques, this dissertation has also pointed out how intrinsically delicate the implementation of Importance Sampling is and has appositely dedicated several sections to important matters that have ...

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Hybrid Multiscale Methods for Hyperbolic and Kinetic
Problems

Hybrid Multiscale Methods for Hyperbolic and Kinetic Problems

... acceptance-rejection techniques described above is the large variance of the samples we ...stratified sampling is to divide the sampling interval up into ...transform sampling or an ...

34

CAPABILITIES AND APPLICATIONS OF PROBABILISTIC METHODS IN FINITE ELEMENT ANALYSIS

CAPABILITIES AND APPLICATIONS OF PROBABILISTIC METHODS IN FINITE ELEMENT ANALYSIS

... is Monte Carlo ...than Monte Carlo simulation ...efficient sampling techniques have been developed such as adaptive importance sampling procedures ...

6

Analytic Method for Probabilistic Cost. and Schedule Risk Analysis. Final Report

Analytic Method for Probabilistic Cost. and Schedule Risk Analysis. Final Report

... Estimates of cost and schedule duration of a task or project are uncertain values, so we do not know the exact, discrete values until it is complete. Given the inherent uncertainty of estimates, the only way to portray ...

190

Stochastic gradient Markov chain Monte Carlo

Stochastic gradient Markov chain Monte Carlo

... for Monte Carlo sampling, which is known as the unadjusted Langevin ...MCMC techniques, such as piece- wise deterministic MCMC (Fearnhead et ...

31

On extended state space constructions for monte carlo methods

On extended state space constructions for monte carlo methods

... chain Monte Carlo ...of Monte Carlo schemes and shows that they can be viewed as (an approximation to) a special case of the mar- ginalised one-sample importance sampling scheme ...

243

Hybrid importance sampling Monte Carlo approach for yield estimation in circuit design

Hybrid importance sampling Monte Carlo approach for yield estimation in circuit design

... Kriging model 4 times by adding some samples from the region near to the failure thresh- old. Table 4 represents the results of the estimation phase of the ISMC and HISMC algo- rithms. The mean probability, the average ...

23

Advances in Monte Carlo techniques with application to lattice protein aggregation

Advances in Monte Carlo techniques with application to lattice protein aggregation

... some Monte Carlo methods commonly used in molecular modeling, and that are closely related to our ...simulation, Monte Carlo methods will almost always be Markov Chain Monte ...

123

Investigating the construct validity of an electronic in-basket exercise using bias-corrected bootstrapping and Monte Carlo re-sampling techniques

Investigating the construct validity of an electronic in-basket exercise using bias-corrected bootstrapping and Monte Carlo re-sampling techniques

... should be combined into two single factors because there seems to be very little distinction between them. Based on the content, it was deemed theoretically permissible to combine these item pairs. Thus, Motivating ...

17

Monte Carlo MCMC: Efficient Inference by Sampling Factors

Monte Carlo MCMC: Efficient Inference by Sampling Factors

... 2004), higher-order models for dependency pars- ing (Carreras, 2007), entity-wise models for coref- erence (Culotta et al., 2007) and global models of relations (Yao et al., 2010). The increasing sophis- tication of ...

5

Monte Carlo sampling approach to stochastic
programming

Monte Carlo sampling approach to stochastic programming

... It is difficult to point out an exact origin of this method. Variants of this approach were suggested by a number of authors under different names (e.g., Rubinstein and Shapiro (stochastic counterpart method) [15], ...

9

Stochastic comparisons of stratied sampling techniques for some Monte Carlo estimators

Stochastic comparisons of stratied sampling techniques for some Monte Carlo estimators

... functional. Monte Carlo estimation of functionals such as the maximum or the integral of a real valued function f is the subject of a very large number of ...stratified sampling schemes, and will ...

22

Variance Reduction Techniques of Importance Sampling Monte Carlo Methods for Pricing Options

Variance Reduction Techniques of Importance Sampling Monte Carlo Methods for Pricing Options

... importance sampling Monte Carlo methods for pricing ...importance sampling method is used to eliminate the variance caused by the linear part of the logarithmic function of ...

6

Monte Carlo Simulation and Improvement of Variance Reduction Techniques

Monte Carlo Simulation and Improvement of Variance Reduction Techniques

... reduction techniques and their combination on the practical problems are compared and ...importance sampling method with antithetic variable can also improve the effect of the variance reduction on the ...

9

Adaptive Strategy for Stratified Monte Carlo Sampling

Adaptive Strategy for Stratified Monte Carlo Sampling

... of sampling a function with natural strata ...Monte Carlo. There are many good surveys on the topic of stratified sampling for Monte Carlo (Glasserman, 2004; Rubinstein and ...

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Generalizations of the Multivariate Logistic Distribution with Applications to Monte Carlo Importance Sampling

Generalizations of the Multivariate Logistic Distribution with Applications to Monte Carlo Importance Sampling

... This section examines Example 8 in greater detail to demonstrate how the EMVL performs as an importance sampler and how the introduction of a scaling factor, c, can improve importance sampling results. This ...

199

Monte Carlo sampling processes and incentive compatible allocations in large economies

Monte Carlo sampling processes and incentive compatible allocations in large economies

... of Monte Carlo simulation is then used in [13] to characterize when, even in the absence of the usual joint measurability assumption, the standard stochastic framework for many heterogeneous agents facing ...

30

Probabilistic Prognostics and Health Management for Fatigue-critical Components using High-fidelity Models.

Probabilistic Prognostics and Health Management for Fatigue-critical Components using High-fidelity Models.

... monitoring techniques rather than offline nondestructive evalu- ation (NDE) as is the case in a general PHM ...Instead, Monte Carlo techniques are the method of choice in this ...

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