
	Where results make sense

Topic: Monte Carlo algorithm

Related Topics

Computer simulation

Adaptive Management

Ecology

List of environment topics

In the News (Thu 24 Dec 09)

Northern Trust

Marvin Harrison

Peter Chernin

Gary Locke

Match Play

Penelope Cruz

Mickey Rourke

AR Rahman

Danny Boyle

Hugh Jackman

	Monte Carlo method - Wikipedia, the free encyclopedia
		Monte Carlo methods are extremely important in computational physics and related applied fields, and have diverse applications from esoteric quantum chromodynamics calculations to designing heat shields and aerodynamic forms.
		Monte Carlo methods were central to the simulations required for the Manhattan Project, though were strongly limited by the computational tools at the time.
		Uses of Monte Carlo methods require large amounts of random numbers, and it was their use that spurred the development of pseudorandom number generators, which were far quicker to use than the tables of random numbers which had been previously used for statistical sampling.
	www.wikipedia.org /wiki/Monte_Carlo_algorithm (1338 words)

	Monte Carlo method -- Facts, Info, and Encyclopedia article (Site not responding. Last check: 2007-10-08)
		Because of the repetition of algorithms and the large number of calculations involved, Monte Carlo is a method suited to calculation using a (A machine for performing calculations automatically) computer, utilizing many techniques of ((computer science) the technique of representing the real world by a computer program) computer simulation.
		Monte Carlo methods were central to the ((computer science) the technique of representing the real world by a computer program) simulations required for the (A former United States executive agency that was responsible for developing atomic bombs during World War II) Manhattan Project.
		Most Monte Carlo optimisation methods are based on (A stochastic process consisting of a sequence of changes each of whose characteristics (as magnitude or direction) is determined by chance) random walks.
	www.absoluteastronomy.com /encyclopedia/m/mo/monte_carlo_method.htm (1170 words)

	Monte Carlo method (Site not responding. Last check: 2007-10-08)
		Monte Carlo methods are methods for solving various kinds of computational problems by using random numbers (or more often pseudo-random numbers), as opposed to deterministic algorithms.
		Monte Carlo methods are extremelyimportant in computational physics and related appliedfields, and have diverse applications from esoteric quantumchromodynamics calculations to designing heat shields and aerodynamicforms.
		Monte Carlo, which is famous for its casinos, lends its name to this method because of the use of randomness and also the repetitive nature employed to find an approximation to the solution.
	www.therfcc.org /monte-carlo-method-3562.html (918 words)

	Monte Carlo method Article, MonteCarlomethod Information (Site not responding. Last check: 2007-10-08)
		Monte Carlo methods are algorithms for solving various kindsof computational problems by using random numbers (or more often pseudo-random numbers), as opposed to deterministic algorithms.
		Monte Carlo methods are extremelyimportant in computational physics and related appliedfields, and have diverse applications from esoteric quantumchromodynamics calculations to designing heat shields and aerodynamic forms.
		Most Monte Carlo optimisation methods are based on random walks.Essentially, the program will move around a marker in multi-dimensional space, tending to move in directions which lead to alower function, but sometimes moving against the gradient.
	www.anoca.org /methods/random/monte_carlo_method.html (874 words)

	Metropolis-Hastings algorithm - Wikipedia, the free encyclopedia
		In mathematics and physics, the Metropolis-Hastings algorithm is an algorithm used to generate a sequence of samples from the probability distribution of one or more variables.
		This algorithm is an example of a Markov chain Monte Carlo algorithm.
		The Gibbs sampling algorithm is a special case of the Metropolis-Hastings algorithm.
	www.wikipedia.org /wiki/Metropolis-Hastings_Markov_Chain_Monte_Carlo_Sampling (513 words)

	Monte Carlo method - LearnThis.Info Enclyclopedia (Site not responding. Last check: 2007-10-08)
		Perhaps the most famous early use was by Fermi in 1930, when he used a random method to calculate the properties of the newly-discovered neutron.
		Monte Carlo methods were central to the simulations required for the Manhattan Project.
		Quasi-Monte Carlo methods can often be more efficent at numerical integration because the sequence "fills" the area better in a sense and samples more of the most important points that can make the simulation converge to the desired solution more quickly.
	encyclopedia.learnthis.info /m/mo/monte_carlo_method.html (892 words)

	APS - 2005 APS March Meeting PostDeadline - Event - A fermionic quantum Monte-Carlo algorithm without the sign problem (Site not responding. Last check: 2007-10-08)
		Recently, a new fermion QMC algorithm has been discovered in which the fermion determinant may not necessarily factorizable, but can instead be expressed as a product of complex conjugate pairs of eigenvalues, thus eliminating the sign problem for a much wider class of models.
		In this paper, we present general conditions for the applicability of this new algorithm and show that it is deeply related to the time reversal symmetry of the fermion matrix.
		We apply this algorithm to many models of strongly correlated systems, including models with purely repulsive interactions, and study their novel phases, for all doping levels and lattice geometries.
	meetings.aps.org /Meeting/MAR05/Event/22242 (183 words)

	Of monte carlo (Site not responding. Last check: 2007-10-08)
		Chevy Monte Carlo is synonymous with the tradition of NASCAR.
		Monte Carlo methods are ways of solving the reinforcement learning problem based on averaging sample returns.
		Monte Carlo is a neural net based computer backgammon player which has learned to play backgammon by self play.
	getinfoeasy.com /q/of-monte-carlo.html (701 words)

	A Monte Carlo Algorithm Based On A State-Space Decomposition Methodology For Flow Network Reliability Evaluation - ... (Site not responding. Last check: 2007-10-08)
		We show that the resulting Monte Carlo estimator belongs to the variance-reduction family and we give a worst-case bound...
		Bulteau, and M. El Khadiri, A Monte Carlo algorithm based on a state space decomposition methodology for flow network reliability evaluation, Technical Report PI 1012, I.R.I.S.A., Campus de Beaulieu, Rennes, France, 1996.
		3 Monte Carlo estimation of the maximum flow distribution in a..
	citeseer.lcs.mit.edu /bulteau96monte.html (472 words)

	R: Rapidly converging Markov chain Monte Carlo algorithm for Bayesian inference in linear mixed models (Site not responding. Last check: 2007-10-08)
		Prior to the MCMC simulation, the posterior mode of the variance parameters is found using the algorithm of "fastmode.lmm".
		The algorithm is considered to have converged if the relative differences in all parameters from one iteration to the next are less than eps–that is, if all(abs(new-old)
		The algorithm is described in Section 5 of Schafer (1998).
	www.matematik.lu.se /help/R/.R/library/lmm/html/fastmcmc.lmm.html (1035 words)

	An Analysis of a Monte Carlo Algorithm for Estimating the Permanent (Site not responding. Last check: 2007-10-08)
		An Analysis of a Monte Carlo Algorithm for Estimating the Permanent
		Abstract: Karmarkar, Karp, Lipton, Lovász, and Luby proposed a Monte Carlo algorithm for approximating the permanent of a non-negative n x n matrix, which is based on an easily computed, unbiased estimator.
		It is not difficult to construct 0,1-matrices for which the variance of this estimator is very large, so that an exponential number of trials are necessary to obtain a reliable approximation that is within a constant factor of the correct value.
	www.lfcs.informatics.ed.ac.uk /reports/91/ECS-LFCS-91-164 (158 words)

	Monte Carlo Damage Simulation (MCDS) Algorithm
		The fast Monte Carlo damage simulation (MCDS) algorithm provides a very fast quasi-phenomenological method to interpolate damage yields from computationally expensive, but more detailed, track-structure simulations.
		Fast Monte Carlo simulation of DNA damage formed by electrons and light ions.
		A fast Monte Carlo algorithm to simulate the spectrum of DNA damages formed by ionizing radiation.
	rh.healthsciences.purdue.edu /mcds (271 words)

	Monte Carlo methods and the Metropolis algorithm (Site not responding. Last check: 2007-10-08)
		Monte Carlo (MC) methods refer, in a very general sense, to any simulation of an arbitrary system which uses a computer algorithm explicitly dependent on a series of (pseudo)random numbers (see, for example, [
		Whenever the conformation resulting from the attempted move is refused for any of the three possible reasons, then the new conformation of the chain is the same current conformation.
		This is the reason why local moves that are not compatible with the chain conformation or violate the excluded volume condition must be considered during the choice of conformation to be attempted in the simulation in conformational space, even if they will always be refused.
	www.unb.br /ib/cel/chico/artigos/thesis/node6.html (1319 words)

	The Monte Carlo Algorithm With A Pseudo-Random Generator - Traub, Wo'zniakowski (ResearchIndex) (Site not responding. Last check: 2007-10-08)
		We analyze the Monte Carlo algorithm for the approximation of multivariate integrals when a pseudo-random generator is used.
		We prove that as long as a pseudo-random generator is capable of producing only finitely many points, the Monte Carlo algorithm with such a pseudo-random generator fails for L 2 or continuous functions.
		Finally, the Monte Carlo algorithm provides only probabilistic error bounds, which is not a desirable guarantee for problems where...
	citeseer.ist.psu.edu /traub89monte.html (621 words)

	Algorithm Development in the Siepmann Group
		A prime challenge for particle-based simulations is to develop algorithms that allow the system to jump directly from one important region to another.
		This is usually achieved by special Monte Carlo algorithms that use specific biasing schemes to locate configurations that make siginificant contributions to the phase space averages.
		Self-adapting fixed-endpoint configurational-bias Monte Carlo method for the regrowth of interior segments of chain molecules with strong intramolecular interactions
	www.chem.umn.edu /groups/siepmann/research/algorithm.html (635 words)

	Monte Carlo algorithm (Site not responding. Last check: 2007-10-08)
		Definition: A randomized algorithm that may produce incorrect results, but with bounded error probability.
		A Monte Carlo algorithm gives more precise results the longer you run it.
		Algorithms and Theory of Computation Handbook, CRC Press LLC, 1999, "Monte Carlo algorithm", from Dictionary of Algorithms and Data Structures, Paul E. Black, ed., NIST.
	www.nist.gov /dads/HTML/monteCarlo.html (164 words)

	Monte Carlo Methods (Site not responding. Last check: 2007-10-08)
		A linear algebra processor using Monte Carlo methods...
		Hénon Monte Carlo method for stellar cluster dynamics...
		Peter Nightingale: Monte Carlo methods in quantum mechanics and statistical mech...
	www.scienceoxygen.com /chem/243.html (123 words)

	Water Science and Technology 36:5 (1997) 141-148 - A. Mailhot et al. - Uncertainty analysis of calibrated parameter ...
		In order to further investigate the lack of data and data uncertainty impacts on calibration, we used a new methodology based on the Metropolis Monte Carlo algorithm.
		This analysis shows that for a large amount of calibration data generated by the model itself, small data uncertainties are necessary to significantly decrease calibrated parameter uncertainties.
		This also confirms the usefulness of the Metropolis algorithm as a tool for uncertainty analysis in the context of model calibration.
	www.iwaponline.com /wst/03605/wst036050141.htm (232 words)

	Markov Chain Monte Carlo - Monaco Center
		… Markov chain Monte Carlo (MCMC) in the form of the Gibbs sampler and the Metropolis, Metropolis-Hastings, and Metropolis-Hastings-Green algorithms, …
		Markov Chain Monte Carlo: innovations and applications in statistics, physics, and bioinformatics.
		Amazon.com: Books: Markov Chain Monte Carlo in Practice by WR Gilks S. Richardson DJ Spiegelhalter.
	www.host4india.net /markov-chain-monte-carlo.html (361 words)

	DIMACS Theory of Computing Seminar (Site not responding. Last check: 2007-10-08)
		We describe an approximation algorithm A which, given small parameters c and d, runs independent experiments and produces an estimate that is within a factor 1+c of M with probability at least 1-d.
		The number of experiments run by A is proportional to quantities that depend on the distribution Z, which are unknown a priori.
		The algorithm A can be directly used in these applications to produce a provably good estimate while running the minimal number of experiments needed for the particular problem instance.
	www.dimacs.rutgers.edu /Events/1995/Titles/1995/Luby.html (186 words)

	Quasi-Monte Carlo Algorithm Tests
		Some of the results (based on 100 samples) are given in Figure 6, with QRSVN results omitted because they were similar to QRSVT results.
		These algorithms use a subregion adaptive integration method, similar to the one that was effective for the lower dimensional MVN problems (see Genz, 1992, 1993, and Berntsen, Espelid and Genz, 1991), applied to the respective SV-Chi-Normal and SV-t formulations of the MVT problem.
		These results provide strong evidence that multivariate t-probabilities can be robustly and reliably computed at low to moderate accuracy levels in less than a second of workstation time for problems with up to twenty dimensions.
	www.sci.wsu.edu /math/faculty/genz/papers/mvtcmpn/node10.html (640 words)

	About the Low Dose Radiation Research Program (Site not responding. Last check: 2007-10-08)
		A fast and easy-to-implement algorithm to generate the full spectrum of damage configurations produced by ionizing radiation is proposed.
		An attractive feature of the proposed algorithm is that only four adjustable parameters need to be identified in order to simulate the formation of DNA damage.
		The good agreement among the damage yields predicted by the fast and detailed damage-formation algorithms suggests that the small-scale spatial distribution of damage sites is primarily determined by independent and purely stochastic events and processes.
	lowdose.tricity.wsu.edu /investigators/rd_stewart_2003abstractMonteCarlo.html (294 words)

	"Worm" Algorithm in Quantum Monte Carlo Simulations (Site not responding. Last check: 2007-10-08)
		When particles are localized, standard local-update canonical-ensemble algorithms suffer from slowing down due to many one-particle minima in the effective action: probing different classes of trajectories, corresponding to different minima, requires deep under-barrier motion.
		Apart from the configuration space parameterization, a QMC algorithm consists of a number of rules, or updating procedures, which describe how to go from one trajectory to another, by, e.g., changing the number of kinks, their types and time positions.
		Another technique allowing Green function calculations is known as Green-function Monte Carlo (or, more generally, the projection-operator method) [9].
	www.magniel.com /hmtj/papers/pst98b/pst.html (2414 words)

	Brian Caffo's Colloquium (Site not responding. Last check: 2007-10-08)
		For moderately sized tables and/or complex models the computing time to enumerate these tables is often prohibitive.
		Monte Carlo approximations offer a viable alternative provided it is possible to obtain samples from the correct conditional distribution.
		This talk presents an MCMC extension of the importance sampling algorithm of Booth and Butler 1999 by utilizing their rounded normal candidate to update randomly chosen cells while leaving the remainder of the table fixed.
	www.stat.ohio-state.edu /~seminar/2001/STATS/caffo.html (150 words)

	APS - 2005 APS March Meeting PostDeadline - Event - The Truncated Polynomial Expansion Monte Carlo Algorithm for ... (Site not responding. Last check: 2007-10-08)
		Abstract: L9.00012 : The Truncated Polynomial Expansion Monte Carlo Algorithm for Spin-fermion Models: Application to Diluted Magnetic Semiconductors and Manganites
		However, this results in a high computational cost as the computational complexity grows with the 4-th power of the size of the system.
		The Truncated Polynomial Expansion Monte Carlo Algorithm (TPEM), developed by N. Furukawa and Y. Motome (J. Phys.
	meetings.aps.org /Meeting/MAR05/Event/26291 (276 words)

	Association of Genetic Traits to Estimated Haplotypes From SNP Genotypes Using EM Algorithm and Monte Carlo Marcov ... (Site not responding. Last check: 2007-10-08)
		It is common practice nowadays to infer multilocus haplotypes and their population frequencies from SNP genotypes in samples of individuals and nuclear families by maximum likelihood estimation via EM algorithm.
		This is done, using the Monte Carlo Markov Chain technique with the Metropolis-Hastings algorithm.
		We have tested the impact of this Monte Carlo technique on simulated samples of individuals and nuclear families in connection with the sample size and demonstrate use of our method for selected experimental samples.
	www.rzpd.de /ngfn_en/poster/proteomics/51rohde.html (243 words)

Try your search on: Qwika (all wikis)