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Pseudorandom Number Assignment in Statistically Designed Simulation and distribution sampling Experiments

Journal of the American Statistical Association 1978年第363期73卷 504-504页

作者： Schruben, Lee W. Margolin, Barry H. Assistant Professor Department of Operations Research Cornell University Ithaca NY 14850. Mathematical Statistician National Institute of Environmental Health Sciences Biometry Branch Research Triangle Park NC 27709

This research investigates various strategies for assigning pseudorandom numbers to experimental points in statistically designed simulation and distribution sampling experiments. Strategies studied include the widely advocated practices of (i) employing a common set of pseudorandom numbers for all experimental points, and (ii) assigning a unique set of pseudorandom numbers to each experimental point. An alternative, based upon blocking concepts in designed experiments, is devised and shown to improve upon existing recommendations for a wide class of problems. A small simulation, a pilot study of a hospital resource allocation problem, illustrates the new strategy. [ABSTRACT FROM AUTHOR]

关键词： Designed experiments distribution sampling Monte Carlo Simulation Time series analysis Variance reduction techniques.

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Unsupervised Domain Adaptation via Importance sampling

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IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY 2020年第12期30卷 4688-4699页

作者： Xu, Xuemiao He, Hai Zhang, Huaidong Xu, Yangyang He, Shengfeng South China Univ Technol Sch Comp Sci & Engn Guangzhou 510006 Peoples R China South China Univ Technol Guangdong Prov Key Lab Computat Intelligence & Cy Guangzhou 510006 Peoples R China

Unsupervised domain adaptation aims to generalize a model from the label-rich source domain to the unlabeled target domain. Existing works mainly focus on aligning the global distribution statistics between source and target domains. However, they neglect distractions from the unexpected noisy samples in domain distribution estimation, leading to domain misalignment or even negative transfer. In this paper, we present an importance sampling method for domain adaptation (ISDA), to measure sample contributions according to their "informative" levels. In particular, informative samples, as well as outliers, can be effectively modeled using feature-norm and prediction entropy of the network. The importance of information is further formulated as the importance sampling losses in features and label spaces. In this way, the proposed model mitigates the noisy outliers while enhancing the important samples during domain alignment. In addition, our model is easy to implement yet effective, and it does not introduce any extra parameters. Extensive experiments on several benchmark datasets show that our method outperforms state-of-the-art methods under both the standard and partial domain adaptation settings.

关键词： Feature extraction Entropy Tuning Estimation Monte Carlo methods Adaptation models Noise measurement Domain adaptation deep learning distribution sampling

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USING INTEGER PROGRAMMING TECHNIQUES FOR THE SOLUTION OF AN EXPERIMENTAL-DESIGN PROBLEM

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ANNALS OF OPERATIONS RESEARCH 1995年第3期58卷 243-260页

作者： HARRIS, CM HOFFMAN, KL YARROW, LA GEORGE MASON UNIV DEPT OPERAT RES & ENGNFAIRFAXVA 22030 CHESAPEAKE DECIS SCI INC NEW PROVIDENCENJ 07974

Latin hypercube sampling is often used to estimate the distribution function of a complicated function of many random variables. In so doing, it is typically necessary to choose a permutation matrix which minimizes the correlation among the cells in the hypercube layout. This problem can be formulated as a generalized, multi-dimensional assignment problem. For the two-dimensional case, we provide a polynomial algorithm. For higher dimensions, we offer effective heuristic and bounding procedures.

关键词： Assignment problem computer models distribution sampling estimation integer programming large-scale modelling latin hypercube optimization sampling sensitivity analysis

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OBTAINING MINIMUM-CORRELATION LATIN HYPERCUBE sampling PLANS USING AN IP-BASED HEURISTIC

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OR SPEKTRUM 1995年第2-3期17卷 139-148页

作者： HARRIS, CM HOFFMAN, KL YARROW, LA CHESAPEAKE DECIS SCI NEW PROVIDENCENJ 07974 GEORGE MASON UNIV DEPT OPERAT RES & ENGNFAIRFAXVA 22030

The objective of Latin Hypercube sampling is to determine an effective procedure for sampling from a (possibly correlated) multivariate population to estimate the distribution function (or at least a significant number of moments) of a complicated function of its variables. The typical application involves a computer-based model in which it is largely impossible to find a way (closed form or numerical) to do the necessary transformation of variables and where it is expensive to run in terms of computing resources and time. Classical approaches to hypercube sampling have used sophisticated stratified sampling techniques;but such sampling may provide incorrect measures of the output parameters' variances or covariances due to correlation between the sampling pairs. In this work, we offer a strategy which provides a sampling specification minimizing the sum of the absolute values of the pairwise differences between the true and sampled correlation pairs. We show that optimal plans can be obtained for even small sample sizes. We consider the characteristics of permutation matrices which minimize the sum of correlations between column pairs and then present an effective heuristic for solution. This heuristic generally finds plans which match the correlation structure exactly. When it does not, we provide a hybrid lagrangian/heuristic method, which empirically has found the optimal solution for all cases tested.

关键词： ASSIGNMENT PROBLEM COMPUTER MODELS distribution sampling ESTIMATION INTEGER PROGRAMMING LARGE-SCALE MODELING LATIN HYPERCUBE OPTIMIZATION sampling SENSITIVITY ANALYSIS

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VECTOR COMPUTERS, MONTE-CARLO SIMULATION AND REGRESSION-ANALYSIS - AN INTRODUCTION

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MANAGEMENT SCIENCE 1992年第2期38卷 170-181页

作者： KLEIJNEN, JPC ANNINK, B TILBURG UNIV SCH BUSINESS & ECONDEPT INFORMAT SYST & AUDITING5000 LE TILBURGNETHERLANDS

Vector computers provide a new tool for management scientists. The application of that tool requires thinking in vector mode. This mode is examined in the context of Monte Carlo experiments with regression models;these regression models may serve as metamodels in simulation experiments. The vector mode needs to exploit a specific dimension of the Monte Carlo experiment, namely the replicates of that experiment. Taking advantage of the machine architecture gives a code that computes Ordinary Least Squares estimates on a Cyber 205 in only 2% of the time needed on a Vax 8700. For Generalized Least Squares estimates, however, the code runs slower on the Cyber 205 than on the VAX, if the regression model is small;for large models the CYBER 205 runs much faster.

关键词： SUPERCOMPUTERS distribution sampling MULTIVARIATE distribution COMMON SEEDS METAMODEL

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Normal Random Numbers: Using Machine Analysis To Choose the Best Algorithm

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ACM Transactions on Mathematical Software (TOMS) 1977年第4期3卷 346-358页

作者： Payne, W.H. Computer Science Department Washington State University Pullman WA 99164 United States

Criteria for selection of a “best” algorithm change with technology. Algorithms to generate samples from the normal probability distribution were analyzed in a technology-independent empirical Knuth type A algorithm analysis. No conclusion of the best algorithm was reached, but scrutiny of results would lead to selection of the best algorithm for a specific implementation. Further, a known general technique for improving rejection sampling efficiency is applied to sampling from the normal distribution. © 1977, ACM. All rights reserved.

关键词： algorithm analysis computational complexity distribution sampling random numbers

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Reducing fossil fuel-based generation: Impact on wholesale electricity market prices in the North-Italy bidding zone

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ELECTRIC POWER SYSTEMS RESEARCH 2021年 194卷

作者： Flammini, Marco G. Prettico, Giuseppe Mazza, Andrea Chicco, Gianfranco European Commiss Joint Res Ctr JRC Ispra VA Italy Politecn Torino Dipartimento Energia Galileo Ferraris Corso Duca Abruzzi 24 I-10129 Turin TO Italy

Decarbonisation policies aim at reducing fossil fuel based generation in favour of cleaner renewable energy sources. Changes in the generation mix to supply future electricity demand will require tools capable to emulate the bidding behaviour of new generation plants. Price forecasting tools lacking this feature and only based on historical data time series might soon become not satisfactory for this scope. This paper presents a methodology that, by considering hourly electricity generation offers (price, volumes) datasets, allows simulating future electricity wholesale?s prices. This is done by taking into account new generation units and the dismissing of old (coal-based) units according to the demand and generation forecasts in the European Ten Year Network Development Plan (TYNDP) 2030 scenarios. Machine learning, clustering and distribution sampling techniques are used in this work to finally estimate prices distribution in 2030 in the biggest bidding zone of the Italian market. The results suggest that the prices obtained in the different scenarios do converge to those estimated by the TYNDP. The approach used bypasses the need to have access to all the transactions of a given market. Probability distributions are in fact enough in the proposed methodology to achieve similar results to those based on full knowledge of transaction datasets.

关键词： Electricity price Electricity market Energy scenarios Generator bidding Machine learning distribution sampling

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C291. Estimating quantiles by simulation: a brownian motion example

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Journal of Statistical Computation and Simulation 1987年第3期28卷 266-272页

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关键词： distribution sampling Monte-Carlo methods quantile estimation simulation

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