Results 11 to 20 of about 708,503 (346)
A survey of Monte Carlo methods for parameter estimation [PDF]
EURASIP Journal on Advances in Signal Processing, 2020 Statistical signal processing applications usually require the estimation of some parameters of interest given a set of observed data. These estimates are typically obtained either by solving a multi-variate optimization problem, as in the maximum ...
David Luengo +4 more
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Modern Monte Carlo methods for efficient uncertainty quantification and propagation: A survey [PDF]
WIREs Computational Statistics, 2020 Uncertainty quantification (UQ) includes the characterization, integration, and propagation of uncertainties that result from stochastic variations and a lack of knowledge or data in the natural world. Monte Carlo (MC) method is a sampling‐based approach
Jiaxin Zhang
semanticscholar +1 more source
Dal'nevostochnyi Matematicheskii Zhurnal, 2020 The article offers a Monte Carlo cluster method for numerically calculating a statistical sample of the state space of vector models. The statistical equivalence of subsystems in the Ising model and quasi-Markov random walks can be used to increase the efficiency of the algorithm for calculating thermodynamic means.
K.V. Makarova +4 more
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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. +3 more
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The road to a modernized NJOY [PDF]
EPJ Web of ConferencesThe 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 +3 more
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International Astronomical Union Colloquium, 1971 AbstractWe give here a detailed technical description of a Monte Carlo scheme for the dynamical evolution of spherical stellar systems. The philosophy of the method, as well as a few illustrative results, are given elsewhere (Hénon, 1971, hereafter called I).
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Unbiased Markov chain Monte Carlo methods with couplings
Journal of the Royal Statistical Society: Series B (Statistical Methodology), 2020 Markov chain Monte Carlo (MCMC) methods provide consistent approximations of integrals as the number of iterations goes to ∞. MCMC estimators are generally biased after any fixed number of iterations.
P. Jacob, J. O'Leary, Y. Atchadé
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Adaptive Monte Carlo augmented with normalizing flows [PDF]
Proceedings of the National Academy of Sciences of the United States of America, 2021 Significance Monte Carlo methods, tools for sampling data from probability distributions, are widely used in the physical sciences, applied mathematics, and Bayesian statistics.
Marylou Gabrié +2 more
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Quantum speedup of Monte Carlo methods. [PDF]
Proc Math Phys Eng Sci, 2015 Monte Carlo methods use random sampling to estimate numerical quantities which are hard to compute deterministically. One important example is the use in statistical physics of rapidly mixing Markov chains to approximately compute partition functions. In
Montanaro A.
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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
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