PDF-Variance Reduction Techniques This chapter develops methods for increasing the eciency

These meth ods draw on two broad strategies for reducing variance taking advantage of tractable features of a model to adjust or correct simulation outputs and reducing the variability in simulation inputs We discuss control variates antithetic vari. X is a random vector in is a function from to and E Note that could represent the values of a stochastic process at di64256erent points in time For example might be the price of a particular stock at time and might be given by so then is the expe Then the standard simulation algorithm is 1 Generate 2 Estimate with 1 n where 3 Approximate 1001 con64257dence intervals are then given by 945 945 where is the usual estimate of Var based on Y One way to measure the quality of the estimator is G.S. Karlovits, J.C. Adam, Washington State University. 2010 AGU Fall Meeting, San Francisco, CA. Outline. Climate change and uncertainty in the Pacific Northwest. Data, model and methods. Climate data. Pete . Truscott. 1. , . Daniel . Heynderickx. 2. , . Fan . Lei. 3. , . Athina . Varotsou. 4. , . Piers . Jiggens. 5. . and Alain . Hilgers. 5. (1) Kallisto Consultancy , UK; (2) DH Consultancy, Belgium; (3) . 3. . . Empirical . classical PES and typical . procedures . of . optimization. 3.03. Monte Carlo and other heuristic procedures. Exploring n-dimensional space. Exploration of energy landscapes of n-dimensional . (Monaco). Monte Carlo Timeline. 10 June 1215. Monaco is taken by the Genoese. 1489. The King of France, Charles VIII, and the Duke of Savoy recognize the sovereignty of Monaco . 1512. Louis XII, King of France, recognizes the independence of Monaco. SIMULATION. Simulation . of a process . – the examination . of any emulating process simpler than that under consideration. .. Examples:. System’s Simulation such as simulation of engineering systems, large organizational systems, and governmental systems. Simple Monte Carlo . Integration. Suppose . that we pick N random points, . uniformly . distributed in a . multidimensional volume . V . Call . them x. 0. ,… . ; . x. N-1. . Then the basic theorem of Monte . Monte Carlo In A Nutshell. Using a large number of simulated trials in order to approximate a solution to a problem. Generating random numbers. Computer not required, though extremely helpful . A Brief History. Decision Making. Copyright © 2004 David M. Hassenzahl. What is Monte Carlo Analysis?. It is a tool for combining . distributions. , and thereby propagating more than just summary statistics. It uses . Monte . Carlo Simulation. Monte Carlo simulations in PSpice can be run as either:. a worst case analysis where the maximum deviation from the nominal values of each component are used in the calculations. Prompt . Neutron . Emission . During. Acceleration in Fission. T.. . Ohsawa. . Kinki University. Japanese Nuclear Data Committee. IAEA/CRP on PFNS, Vienna, Dec. 13-16, 2011. q. ε. Overall agreement between. José A. Ramos Méndez, PhD.. University of California San Francisco. Outline. Introduction. Basics of Monte Carlo method. Statistical Uncertainty. Improving Efficiency Techniques. 11/2/15. FCFM-BUAP, Puebla, Pue.. 1 June 2015. Contents. Commercial background. Amec. Foster Wheeler. The ANSWERS suite of software. Technical background. The Boltzmann Transport Equation. Deterministic versus Monte Carlo methods. Challenges in Monte Carlo.