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This paper provides an asymptotically efficient algorithm for the allocation of computing resources to the problem of Monte Carlo integration of continuous-time security prices. The tradeoff between i...
This paper is concerned with applying importance sampling as a variance reduction tool for computing extreme quantiles. A central limit theorem is derived for each of four proposed importance sampling...
A constrained Monte Carlo problem arises when one computes an expectation in the presence of a priori computable constraints on the expectations of quantities that are correlated with the estimand. Th...
This paper discusses some connections between adaptive Monte Carlo algorithms and general state space Markov chains. Adaptive algorithms are iterative methods in which previously generated samples are...
We examine different ways of numerically computing the distribution function of conditional expectations where the conditioning element takes values in a finite or countably infinite outcome space. Bo...
In this work we show how to resolve, at least partially, the curse of dimensionality of likelihood ratios (LRs) while using importance sampling (IS) to estimate the performance of high-dimensional Mon...
In this paper we investigate the approximation properties of the coarse-graining procedure applied to kinetic Monte Carlo simulations of lattice stochastic dynamics. We provide both analytical and num...
We propose an efficient Markov Chain Monte Carlo method for sampling equilibrium distributions for stochastic lattice models, capable of handling correctly long and short-range particle interactions. ...
针对部分可观察马尔可夫决策过程(POMDPs) 的信念状态空间是一个双指数规模问题, 提出一种基于Monte Carlo 粒子滤波的POMDPs 在线算法. 首先, 分别采用粒子滤波和粒子映射更新和扩展信念状态, 建立可达信念状态 与或树; 然后, 采用分支界限裁剪方法对信念状态与或树进行裁剪, 降低求解规模. 实验结果表明, 所提出算法具有较 低的误差率和较快的收敛性, 能够满足系统实时性...
We consider the computation of the permanent of a binary n by n matrix. It is well- known that the exact computation is a #P complete problem. A variety of Markov chain Monte Carlo (MCMC) computationa...
We describe a novel Bayesian approach to the estimation of neural currents from a single distribution of magnetic field, measured by magnetoencephalography. We model neural currents as an unknown numb...
We propose a new algorithm to do posterior sampling of Kingman's coalescent, based upon the Particle Markov Chain Monte Carlo methodology. Specifically, the algorithm is an instantiation of the Partic...
Many mathematical models involve input parameters, which are not precisely known. Global sensitivity analysis aims to identify the parameters whose uncertainty has the largest impact on the variabilit...
Model comparison for the purposes of selection, averaging and validation is a problem found throughout statistics and related disciplines. Within the Bayesian paradigm, these problems all require the ...
We consider the problem of adaptive stratified sampling for Monte Carlo integration of a noisy function, given a finite budget n of noisy evaluations to the function. We tackle in this paper the probl...

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