![]() in mathematics with concentrations in statistics and economics from Auburn University Montgomery and is currently pursuing a master’s degree in applied economics at Johns Hopkins University. He presented on the topic of risk analysis at the 2010 SCEA conference, and is a published author in the field of economics with papers involving computation of equilibriums in industrial organization models. Blake’s research interests include applied probability, computational mathematics, and math modeling and simulation. He is the lead developer for multiple cost and risk estimating tools and is involved with efforts to advance the application of numerical methods to risk analysis. Blake currently supports a variety of cost estimating projects for the USMC. The next two lines set the number of trials in the simulation to 5000, and the sampling method to Latin Hypercube. He is a firm subject matter expert in the areas of quantitative risk analysis and Microsoft Excel development. The first two lines create an instance of a Problem, and initialize it with the simulation model defined in your Excel workbook. Author:īlake Boswell is a Consultant for Booz Allen Hamilton’s Business Analytics Team. The efficiency of the sampling techniques is tested within the framework of cost risk analysis through the implementation of probabilistic distributions common to the field and through the development of code in VBA and VC++ which enables the techniques to be called from Microsoft Excel. LHHS is a relatively new sampling method which is a hybrid of the well established methods: Latin-Hypercube Sampling and Hammersley Sequences. Sobol Sequences are widely deployed in QMC simulation, but are not a standard sampling option in COTS risk analysis software commonly used in cost analysis. In this paper, two QMC sampling techniques are investigated within the framework of cost risk analysis: Sobol Sequences and Latin Hypercube Hammersley Sequences (LHHS). To combat increasing computational complexity in probabilistic numerical models, Quasi-Monte Carlo (QMC) sampling techniques employ systematic approaches to random sampling with the goal of achieving accurate statistical measures while using fewer samples than traditional MC methods. The method commonly used to reduce the number or runs necessary for a Monte Carlo simulation to achieve a reasonably accurate random distribution. Therefore, in probabilistic numerical methods, a trade-off exits between accuracy of results and computational complexity. Latin hypercube sampling (LHS) is a form of stratified sampling that can be applied to multiple variables. Such methods involve modeling risk and uncertainty as probabilistic distributions, applying iterative sampling techniques using Monte Carlo (MC) methods, and deriving risk and uncertainty adjusted statistical measures however, the accuracy of statistical measures resulting from probabilistic analyses is directly dependent on the number of samples considered. RS02_Presentation_QuasiMonteCarloMethods_Boswell Abstract:Ĭost analyses rely on probabilistic numerical methods to estimate the impact of risk and uncertainty associated with systems technical definitions and cost estimating methodologies. Quasi-Monte Carlo Methods: Combating Complexity in Cost Risk Analysis
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