Results 11 to 20 of about 876,447 (311)
Population size bias in diffusion Monte Carlo [PDF]
Physical Review E, 2012 The size of the population of random walkers required to obtain converged estimates in DMC increases dramatically with system size. We illustrate this by comparing ground state energies of small clusters of parahydrogen (up to 48 molecules) computed by Diffusion Monte Carlo (DMC) and Path Integral Ground State (PIGS) techniques.
Boninsegni M, Moroni S
openaire +4 more sources
Statistical Inference for Partially Observed Markov Processes via the R Package pomp [PDF]
Journal of Statistical Software, 2016 Partially observed Markov process (POMP) models, also known as hidden Markov models or state space models, are ubiquitous tools for time series analysis.
Aaron A. King +2 more
doaj +5 more sources
Bayesian model comparison in cosmology with Population Monte Carlo [PDF]
, 2009 We use Bayesian model selection techniques to test extensions of the standard flat Λ cold dark matter (ΛCDM) paradigm. Dark-energy and curvature scenarios, and primordial perturbation models are considered. To that end, we calculate the Bayesian evidence
M. Kilbinger +11 more
semanticscholar +4 more sources
dart_board: Binary Population Synthesis with Markov Chain Monte Carlo [PDF]
The Astrophysical Journal Supplement Series, 2017 By employing Monte Carlo random sampling, traditional binary population synthesis (BPS) offers a substantial improvement in efficiency over brute force, grid-based studies.
J. Andrews, A. Zezas, T. Fragos
semanticscholar +5 more sources
Parameter Estimation in Population Balance through Bayesian Technique Markov Chain Monte Carlo [PDF]
Journal of Applied and Computational Mechanics, 2021 In this work, the Markov Chain Monte Carlo is applied to estimate parameters that represent mechanisms that describe particles' dynamics in particulate systems from the literature's proposed models.
Carlos H.R. Moura +5 more
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Monte Carlo simulation with confusion matrix paradigm – A sample of internal consistency indices
Frontiers in Psychology, 2023 Monte Carlo simulation is a common method of providing empirical evidence to verify statistics used in psychological studies. A representative set of conditions should be included in simulation studies.
Yongtian Cheng +3 more
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Scientific Reports, 2023 Bernstein fits implemented into R allow another route for Kruskal-Wallis power-study tool development. Monte-Carlo Kruskal-Wallis power studies were compared with measured power, a Monte-Carlo ANOVA equivalent and with an analytical method, with or ...
Jeremy S. C. Clark +7 more
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Population Markov Chain Monte Carlo [PDF]
Machine Learning, 2003 zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Laskey, Kathryn Blackmond +1 more
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Alexandria Engineering Journal, 2022 In a life testing experiments, accelerated life tests (ALTs) model has provided a significant decrease for the cost and time. The problem of statistical inference of constant-stress ALTs based on censored data is discussed in this paper. So, we implement
Saad J. Almalki +3 more
doaj +1 more source
Journal of Clinical and Translational Science, 2020 OBJECTIVES/GOALS: The Monte Carlo dose calculation method is often considered the “gold standard” for patient dose calculations and can be as radiation dose measurements.
Holly Marie Parenica +6 more
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