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An Optimal Algorithm for Monte Carlo Estimation
, 1995
"... A typical approach to estimate an unknown quantity is to design an experiment that produces a random variable Z distributed in [0; 1] with E[Z] = , run this experiment independently a number of times and use the average of the outcomes as the estimate. In this paper, we consider the case when no a ..."
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Cited by 68 (4 self)
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prove that the expected number of experiments run by AA (which depends on Z) is optimal to within a constant factor for every Z. An announcement of these results appears in P. Dagum, D. Karp, M. Luby, S. Ross, "An optimal algorithm for MonteCarlo Estimation (extended abstract)", Proceedings
Nonlinear Monte Carlo Estimators for Parabolic Equation
"... We consider the parabolic type equation with a sourcesink term and construct the Monte Carlo estimator for it. The procedure is based on the HopfCole transformation and a Monte Carlo estimator for the correspondent Burgers equation. ..."
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We consider the parabolic type equation with a sourcesink term and construct the Monte Carlo estimator for it. The procedure is based on the HopfCole transformation and a Monte Carlo estimator for the correspondent Burgers equation.
On The Existence Of Unbiased Monte Carlo Estimators
, 1995
"... For many typical instances where Monte Carlo methods are applied attempts were made to find unbiased estimators, since for them the Monte Carlo error reduces to the statistical error. These problems usually take values in the scalar field. If we study vector valued Monte Carlo methods, then we are c ..."
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For many typical instances where Monte Carlo methods are applied attempts were made to find unbiased estimators, since for them the Monte Carlo error reduces to the statistical error. These problems usually take values in the scalar field. If we study vector valued Monte Carlo methods, then we
Markov Chain Monte Carlo Estimation of
"... Much of nonlinear time series analysis is concerned with inferring unmeasured quantities  e.g., system parameters, the shape of attractors in state space  from a noisy measured time series. From a Bayesian perspective, the time series is a vector sample picked at random from a probability dens ..."
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parameters. Using illustrative chaotic systems with largeamplitude dynamical and measurement noise, we show here that it is feasible to use the Markov chain Monte Carlo (MCMC) technique to generate the Bayesian conditional probabilities. The resulting parameter estimates are markedly superior to those based
Task Scheduling Based on Degenerated Monte Carlo Estimate in
"... Mobile cloud computing, which comes up in recent years, is a new computing paradigm. It enables people to access remote clouds by mobile device, even to build mobile microcloud(MuCloud) with mobile device to provide lightweight service. Despite extensive studies of task scheduling in wired cloud, e ..."
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for selection of makespan and load balancing as two key performance indicators for task scheduling in the proposed architecture of mobile cloud which integrates MuClouds. Then after introduction to Monte Carlo method, degenerated Monte Carlo estimate is defined and a scheduling algorithm based on degenerated
Large Sample Properties of Weighted Monte Carlo Estimators
 Working Paper DRO200207, Columbia Business School
, 2003
"... A general approach to improving simulation accuracy uses information about auxiliary control variables with known expected values to improve the estimation of unknown quantities. We analyze weighted Monte Carlo estimators that implement this idea by applying weights to independent replications. The ..."
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Cited by 9 (1 self)
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A general approach to improving simulation accuracy uses information about auxiliary control variables with known expected values to improve the estimation of unknown quantities. We analyze weighted Monte Carlo estimators that implement this idea by applying weights to independent replications
Monte Carlo Estimation of Bayesian Credible and HPD Intervals
 Journal of Computational and Graphical Statistics
, 1998
"... This paper considers how to estimate Bayesian credible and highest probability density (HPD) intervals for parameters of interest and provides a simple Monte Carlo approach to approximate these Bayesian intervals when a sample of the relevant parameters can be generated from their respective margina ..."
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Cited by 60 (4 self)
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This paper considers how to estimate Bayesian credible and highest probability density (HPD) intervals for parameters of interest and provides a simple Monte Carlo approach to approximate these Bayesian intervals when a sample of the relevant parameters can be generated from their respective
Importance sampling for Monte Carlo estimation of quantiles
 Mathematical Methods in Stochastic Simulation and Experimental Design: Proceedings of the 2nd St. Petersburg Workshop on Simulation
, 1996
"... 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 quantile estimators. Efficiency comparisons are provided in a certain asymptotic setting, using id ..."
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Cited by 24 (0 self)
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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 quantile estimators. Efficiency comparisons are provided in a certain asymptotic setting, using
Monte Carlo Estimation Of Morphological Granulometric Discrete Size Distributions
 Johns Hopkins University
, 1994
"... . Morphological granulometries are frequently used as descriptors of granularity, or texture, within a binary image. In this paper, we study the problem of estimating the (discrete) size distribution and size density of a random binary image by means of empirical, as well as, Monte Carlo estimators. ..."
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Cited by 2 (2 self)
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. Morphological granulometries are frequently used as descriptors of granularity, or texture, within a binary image. In this paper, we study the problem of estimating the (discrete) size distribution and size density of a random binary image by means of empirical, as well as, Monte Carlo estimators
Results 1  10
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435,600