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Monte Carlo Method

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		A Probability Density Function (PDF) Method for Turbulent Reacting Flow ^Votes:0 Glenn Research Center 1990 Phase II A Probability Density Function (PDF) Method for Turbulent Reacting Flow CFD Research Corporation Huntsville, AL INNOVATION A Monte Carlo simulation technique for turbulent combustion and its viability for practical applications Monte Carlo PDF solution agrees with experimental data (Piloted Jet Diffusion Flame) much better than solutions using simpler models Optional Powerpoint file ACCOMPLISHMENTS A Monte Carlo solution module for the composition PDF was developed to solve finite-rate chemical kinetics in turbulent flows The PDF module was coupled with a general purpose CFD code, CFD-ACE The PDF module was validated against experimental data for hydrogen and hydrocarbon combustion COMMERCIALIZATION The PDF solution module has been incorporated into CFD- Read More Go to Site


		Buffon's Needle ^Votes:0 Buffon's Needle Buffon's Needle Buffon's Needle refers to a simple Monte Carlo method for the estimation of the value of pi, 3.14159265... The idea is very simple. Suppose you have a tabletop with a number of parallel lines drawn on it, which are equally spaced (say the spacing is 1 inch, for example). Suppose you also have a pin or needle, which is also an inch long. If you drop the needle on the table, you will find that one of two things happens: (1) The needle crosses or touches one of the lines, or (2) the needle crosses no lines. The idea now is to keep dropping this needle over and over on the table, and to record the statistics. Namely, we want to keep track of both the total number of times that the needle is randomly dropped on the table (call this N), and the number of times tha Read More Go to Site

		Columbia Receives Patent For Fast Method To Value ComplexSecurities ^Votes:0 Contact: Bob Nelson (212) 854-6580 rjn2@columbia.edu For immediate release August 16, 1999 Columbia Receives Patent For Fast Method To Value Complex Securities Three New York inventors have shown that complex securities can be valued much faster and more accurately than by the method widely used by financial institutions. Joseph F. Traub, the Edwin Howard Armstrong Professor of Computer Science at Columbia University; Spassimir Paskov, a former Ph.D. student of Professor Traub's, now associate director in risk management at the New York office of Barclays Capital, the investment arm of Barclays Bank; and Irwin Vanderhoof, professor of finance at New York University, have received a patent to be issued Aug. 17. (Patent # 5,940,819) The patent has been assigned to Columbia University, where Read More Go to Site

		CPS 713 --- Monte Carlo Simulation for Statistical Physics ^Votes:0 Next: Statistical Mechanics CPS 713 Monte Carlo Simulation for Statistical Physics Paul Coddington Northeast Parallel Architectures Center at Syracuse University Statistical Mechanics Statistical Physics Stochastic (probabilistic) Processes Systems with Many Degrees of Freedom Applications of Statistical Mechanics Applications of Statistical Mechanics (cont.) Calculations in Statistical Mechanics Spin Models Example: Spin Models of Magnetism Magnetic Phase Transitions Simple Model of Magnetism The Ising Model Ising Ferromagnet Solution of the Ising Model Statistical Mechanics of Spin Models The Boltzmann Distribution Monte Carlo Methods Review of Monte Carlo Integration Monte Carlo Simulation Calculating the Partition Function Importance Sampling Markov Processes Detailed Balance The Metro Read More Go to Site

		MAT310 Monte Carlo ^Votes:0 MAT310, Numerical Analysis: Laboratory 5: Monte Carlo Methods Introduction and Motivation Calculating PI Calculating Definite Integrals Percolation Problems Atomic Diffusion Pseudorandom Number Generators Laboratory Exercises Testing the Pseudorandom Number Generator Estimate for the value of PI Calculation of an Integral using the Monte Carlo approach Discussion Questions Read More Go to Site

		Monte Carlo and Quasi-Monte Carlo ^Votes:0 Quasi-Monte Carlo (QMC) has been shown to be superior to Monte Carlo (MC) for financial computations . This may be due to the non-isotropic nature of high-dimensional integrals arising in finance. Recently we tested an isotropic problem from physics and found that again QMC was far superior to MC. See Faster Evaluation of Multidimensional Integrals , Computers in Physics, November, 1997, 574-578 (with A. Papageorgiou). An open question is to characterize for which classes of integrals QMC is superior to MC. The expected error of MC for computing multivariate integrals is proportional to n -1/2 if the integrand is evaluated at n randomly chosen points. What is the expected error if pseudo-random points are used? An analysis may be found in The Monte Carlo Algorithm with a Pseudo-random Gene Read More Go to Site

		MONTE CARLO METHOD ^Votes:0 HISTORY OF MONTE CARLO METHOD by Sabri Pllana \| Home \| History \| Buffon \| Links \| E-mail \| Read More Go to Site

		MONTE CARLO METHOD ^Votes:0 HISTORY OF MONTE CARLO METHOD by Sabri Pllana \| Home \| History \| Buffon \| Links \| E-mail \| Read More Go to Site

		MONTE CARLO METHOD ^Votes:0 HISTORY OF MONTE CARLO METHOD by Sabri Pllana \| Home \| History \| Buffon \| Links \| E-mail \| Read More Go to Site

		Monte Carlo Method and Transport Equation in Plant Canopies ^Votes:0 Monte Carlo Method and Transport Equation in Plant Canopies Monte Carlo Method and Transport Equation in Plant Canopies Authors: V.S. Antyufeev and A.L. Marshak Journal: Remote Sens. Environ. , 31 , 183-191, 1990. Abstract: Plant canopy reflectance is calculated using the governing equation for photon transport. The integral equation of transfer is solved by the Monte Carlo method. The main emphasis is on statistical estimation and simulation of the Marcov chain. The leaf dimensions are taken into account in obtaining the hot-spot effect of the canopy. Finally, numerical results for transport equation obtained by the Monte Carlo method are compared with those obtained by using the method of discrete ordinates and the geometrical Monte Carlo model. Read More Go to Site

		Monte Carlo Methods in Parallel Computing ^Votes:0 Monte Carlo Methods in Parallel Computing Chuanyi Ding ding@arc.unm.edu Eric Haskin haskin@unm.edu Copyright by UNM/ARC November 1995 Outline What Is Monte Carlo? Example 1 - Monte Carlo Integration To Estimate Pi Example 2 - Monte Carlo solutions of Poisson's Equation Example 3 - Monte Carlo Estimates of Thermodynamic Properties General Remarks on Parallel Monte Carlo What is Monte Carlo? A powerful method that can be applied to otherwise intractable problems A game of chance devised so that the outcome from a large number of plays is the value of the quantity sought On computers random number generators let us play the game The game of chance can be a direct analog of the process being studied or artificial Different games can often be devised to solve the same problem The art of Monte C Read More Go to Site

		Monte Carlo Simulation ^Votes:0 Monte Carlo Simulation So what does a computer simulation technique have in common with the world famous Monte Carlo casino, in the second smallest country in the world, the principality of Monaco ? - The element of chance is used in both, so that the desired result occurs in the long run. The random nature of a game of chance is designed so that the owners of the casino can be assured, that in the long run, the casino will make a profit while the individual gambler has a reasonable chance of winning. The random nature of a Monte Carlo simulation is designed so that the programmer can be assured, that in the long run, the simulation will approach equilibrium values, while an individual move has a realistic chance of taking the simulation away from equilibrium. Monte Carlo simulation method Read More Go to Site

		SEQuaL Project: Lessons - Monte Carlo Simulation ^Votes:0 Monte Carlo Simulation Chris Brueninqsen Grade 10, Probability & Statistics Monte Carlo simulation provides an easy method for estimating probabilities which are difficult to compute analytically. It is based on the notion that when the simulation is repeated many times, the probability, p, is approximately: number of successes/number of trials Rolling Two Dice. Estimate the chance of rolling a 7 using two dice. On the TI-82, enter: seq(iPart(rand*6 + 1), X, 1, 99, 1) sto L1 2nd ENTER sto L2 L1 + L2 sto L3 Stat Plot: Type = histogramWindow: X [0, 13] Xlist = L3Y [O, 25] Freq = 1Xscl = 1 Move cursor to min = 7 and compute p = n/99 Compare to the theoretical value, p = 1/6. The Dartboard Problem. A dart board consists of a circle inscribed in a square. If darts are randomly thrown at the boa Read More Go to Site

		Wave:Monte Carlo ^Votes:0 Wavelet Analysis Introduction Wavelets Algorithms Monte Carlo References [ back to PAOS ] Monte Carlo Method Important Note: When applying the significance and confidence tests from "A Practical Guide to Wavelet Analysis" , you do not need to use a Monte Carlo simulation. Analytical formulae are given in the paper for the statistical distribution of wavelet power. These formulae are correct, assuming that the underlying distribution of the original time series is Gaussian. The following description of Monte Carlo is given for those who want more background on the method, or if your time series cannot be assumed to be Gaussian. Gaussian distribution &nbsp &nbsp &nbsp The Monte Carlo method (or simulation) was used in "A Practical Guide to Wavelet Analysis" to verify that the wavelet power s Read More Go to Site

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