Search results
Results from the WOW.Com Content Network
Monte Carlo simulation: Drawing a large number of pseudo-random uniform variables from the interval [0,1] at one time, or once at many different times, and assigning values less than or equal to 0.50 as heads and greater than 0.50 as tails, is a Monte Carlo simulation of the behavior of repeatedly tossing a coin.
Markov chain quasi-Monte Carlo methods [18] [19] such as the Array–RQMC method combine randomized quasi–Monte Carlo and Markov chain simulation by simulating chains simultaneously in a way that better approximates the true distribution of the chain than with ordinary MCMC. [20]
The Monte Carlo method encompasses any technique of statistical sampling employed to approximate solutions to quantitative problems. [5] Essentially, the Monte Carlo method solves a problem by directly simulating the underlying (physical) process and then calculating the (average) result of the process. [1]
Monte Carlo simulation is a mathematical technique for considering the effect of uncertainty on investing as well as many other activities. A Monte Carlo simulation shows a large number and ...
Monte Carlo simulated stock price time series and random number generator (allows for choice of distribution), Steven Whitney; Discussion papers and documents. Monte Carlo Simulation, Prof. Don M. Chance, Louisiana State University; Pricing complex options using a simple Monte Carlo Simulation, Peter Fink (reprint at quantnotes.com)
The antithetic variates technique consists, for every sample path obtained, in taking its antithetic path — that is given a path {, …,} to also take {, …,}.The advantage of this technique is twofold: it reduces the number of normal samples to be taken to generate N paths, and it reduces the variance of the sample paths, improving the precision.
Direct simulation Monte Carlo (DSMC) method uses probabilistic Monte Carlo simulation to solve the Boltzmann equation for finite Knudsen number fluid flows. The DSMC method was proposed by Graeme Bird, [ 1 ] [ 2 ] [ 3 ] emeritus professor of aeronautics, University of Sydney.
Modeling photon propagation with Monte Carlo methods is a flexible yet rigorous approach to simulate photon transport. In the method, local rules of photon transport are expressed as probability distributions which describe the step size of photon movement between sites of photon-matter interaction and the angles of deflection in a photon's trajectory when a scattering event occurs.