Results 21 to 30 of about 831,537 (291)

Monte Carlo methods

EPJ Web of Conferences, 2013
Bayesian inference often requires integrating some function with respect to a posterior distribution. Monte Carlo methods are sampling algorithms that allow to compute these integrals numerically when they are not analytically tractable.
Bardenet Rémi
doaj   +1 more source

Neutron Cross Section Processing Methods for Improved Integral Benchmarking of Unresolved Resonance Region Evaluations

EPJ Web of Conferences, 2016
In this work we describe the development and application of computational methods for processing neutron cross section data in the unresolved resonance region (URR). These methods are integrated with a continuous-energy Monte Carlo neutron transport code,
Walsh Jonathan A., Forget Benoit, Smith Kord S., Brown Forrest B.   +3 more
doaj   +1 more source

The road to a modernized NJOY [PDF]

EPJ Web of Conferences
The modernized version of the NJOY Nuclear Data Processing System is being built from a series of components that enable the traditional work required of the production version of NJOY while also providing a much more interactive user experience through ...
Haeck Wim, Gibson Nathan, Talou Patrick, Josey Colin   +3 more
doaj   +1 more source

A New Monte Carlo Method for Time-Dependent Neutrino Radiation Transport [PDF]

, 2012
Monte Carlo approaches to radiation transport have several attractive properties compared to deterministic methods. These include simplicity of implementation, high accuracy, and good parallel scaling.
Adam Burrows, Bruenn, Bruenn, Burrows, Burrows, Burrows, Christian D. Ott, Erik Schnetter, Ernazar Abdikamalov, Evan O'Connor, Frank Löffler, Gentile, Goodale, Hanke, Hillebrandt, Hubeny, Hummer, Janka, Janka, Janka, Janka, Joshua C. Dolence, Kalos, Kasen, Keil, Liebendörfer, Liebendörfer, Marek, Martin, Mezzacappa, Mihalas, Morel, Murphy, Nordhaus, Ott, Pejcha, Pomraning, Rampp, Smit, Sumiyoshi, Sumiyoshi, Suwa, Swesty, Takahashi, Takiwaki, Thompson, Thompson, Wollaber, Wollaber, Yakunin, Yamada, Zink   +51 more
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Multilevel Monte Carlo methods [PDF]

Acta Numerica, 2013
Monte Carlo methods are a very general and useful approach for the estimation of expectations arising from stochastic simulation. However, they can be computationally expensive, particularly when the cost of generating individual stochastic samples is very high, as in the case of stochastic PDEs.
openaire   +3 more sources

Numerical approximation of statistical solutions of scalar conservation laws

, 2014
We propose efficient numerical algorithms for approximating statistical solutions of scalar conservation laws. The proposed algorithms combine finite volume spatio-temporal approximations with Monte Carlo and multi-level Monte Carlo discretizations of ...
Barrett, Paul M., Butler, Richard J., Irmis, Randall B., Mundil, Roland, Scheyer, Torsten M., Sánchez-Villagra, Marcelo R.   +5 more
core   +3 more sources

Monte Carlo methods for TMD analyses

EPJ Web of Conferences, 2015
Monte Carlo simulations are an indispensable tool in experimental high-energy physics. Indeed, many discoveries rely on realistic modeling of background processes.
Schnell Gunar
doaj   +1 more source

Monte Carlo Methods and the Koksma-Hlawka Inequality

Mathematics, 2019
The solution of a wide class of applied problems can be represented as an integral over the trajectories of a random process. The process is usually modeled with the Monte Carlo method and the integral is estimated as the average value of a certain ...
Sergey Ermakov, Svetlana Leora
doaj   +1 more source

Extending canonical Monte Carlo methods II [PDF]

, 2010
Previously, we have presented a methodology to extend canonical Monte Carlo methods inspired on a suitable extension of the canonical fluctuation relation $C=\beta^{2}$ compatible with negative heat capacities ...
Challa M S S, Dress C, Gross D H E, Kasteleyn P W, L Velazquez, Landau D P, S Curilef, Velazquez L, Velazquez L, Velazquez L, Velazquez L   +10 more
core   +3 more sources

Bayesian statistics and Monte Carlo methods

Journal of Geodetic Science, 2018
The Bayesian approach allows an intuitive way to derive the methods of statistics. Probability is defined as a measure of the plausibility of statements or propositions. Three rules are sufficient to obtain the laws of probability.
Koch K. R.
doaj   +1 more source