Accelerating HEP simulations with Neural Importance Sampling
Sign Up to like & getrecommendations! Published in 2024 at "Journal of High Energy Physics"
DOI: 10.1007/jhep03(2024)083
Abstract: Many high-energy-physics (HEP) simulations for the LHC rely on Monte Carlo using importance sampling by means of the VEGAS algorithm. However, complex high-precision calculations have become a challenge for the standard toolbox, as this approach… read more here.
Keywords: importance sampling; accelerating hep; neural importance; hep simulations ... See more keywords
Support vector machine-based importance sampling for rare event estimation
Sign Up to like & getrecommendations! Published in 2021 at "Structural and Multidisciplinary Optimization"
DOI: 10.1007/s00158-020-02809-8
Abstract: Structural reliability analysis aims at computing failure probability with respect to prescribed performance function. To efficiently estimate the structural failure probability, a novel two-stage meta-model importance sampling based on the support vector machine (SVM) is… read more here.
Keywords: model; importance; importance sampling; failure probability ... See more keywords
The Importance of Importance Sampling: Exploring Methods of Sampling from Alternatives in Discrete Choice Models of Crime Location Choice
Sign Up to like & getrecommendations! Published in 2021 at "Journal of Quantitative Criminology"
DOI: 10.1007/s10940-021-09526-5
Abstract: The burgeoning field of individual level crime location choice research has required increasingly large datasets to model complex relationships between the attributes of potential crime locations and offenders’ choices. This study tests methods of sampling… read more here.
Keywords: location choice; crime location; choice; importance sampling ... See more keywords
Layered adaptive importance sampling
Sign Up to like & getrecommendations! Published in 2017 at "Statistics and Computing"
DOI: 10.1007/s11222-016-9642-5
Abstract: Monte Carlo methods represent the de facto standard for approximating complicated integrals involving multidimensional target distributions. In order to generate random realizations from the target distribution, Monte Carlo techniques use simpler proposal probability densities to… read more here.
Keywords: importance sampling; adaptive importance; monte carlo; importance ... See more keywords
Importance sampling for partially observed temporal epidemic models
Sign Up to like & getrecommendations! Published in 2019 at "Statistics and Computing"
DOI: 10.1007/s11222-018-9827-1
Abstract: We present an importance sampling algorithm that can produce realisations of Markovian epidemic models that exactly match observations, taken to be the number of a single event type over a period of time. The importance… read more here.
Keywords: importance; time; partially observed; importance sampling ... See more keywords
Adaptive Kriging coupled with importance sampling strategies for time-variant hybrid reliability analysis
Sign Up to like & getrecommendations! Published in 2020 at "Applied Mathematical Modelling"
DOI: 10.1016/j.apm.2019.08.025
Abstract: Abstract For the time-variant hybrid reliability problem under random and interval uncertainties, the upper bound of time-variant failure probability, as a conservative index to quantify the safety level of the structure, is highly concerned. To… read more here.
Keywords: importance; hybrid reliability; time variant; importance sampling ... See more keywords
An efficient method combining polynomial-chaos kriging and adaptive radial-based importance sampling for reliability analysis
Sign Up to like & getrecommendations! Published in 2021 at "Computers and Geotechnics"
DOI: 10.1016/j.compgeo.2021.104434
Abstract: Abstract This paper develops an efficient algorithm that combines polynomial-chaos kriging (PCK) and adaptive radial-based importance sampling (ARBIS) for reliability analysis. The key idea of ARBIS is to adaptively determine a sphere with the center… read more here.
Keywords: chaos kriging; radial based; adaptive radial; polynomial chaos ... See more keywords
Adaptive importance sampling and control variates
Sign Up to like & getrecommendations! Published in 2020 at "Journal of Mathematical Analysis and Applications"
DOI: 10.1016/j.jmaa.2019.123608
Abstract: Abstract We construct and investigate an adaptive variance reduction framework in which both importance sampling and control variates are employed. The three lines (Monte Carlo averaging and two variance reduction parameter search lines) run in… read more here.
Keywords: control variates; variance; importance sampling; variance reduction ... See more keywords
An active learning Kriging model combined with directional importance sampling method for efficient reliability analysis
Sign Up to like & getrecommendations! Published in 2020 at "Probabilistic Engineering Mechanics"
DOI: 10.1016/j.probengmech.2020.103054
Abstract: Abstract In this paper, the active learning Kriging model (ALK), which has been studied extensively in recent years, has been expanded by combining with the directional importance sampling (DIS) method. The directional sampling method can… read more here.
Keywords: importance sampling; reliability analysis; method; directional importance ... See more keywords
Moderate deviations-based importance sampling for stochastic recursive equations
Sign Up to like & getrecommendations! Published in 2017 at "Advances in Applied Probability"
DOI: 10.1017/apr.2017.31
Abstract: Abstract Subsolutions to the Hamilton–Jacobi–Bellman equation associated with a moderate deviations approximation are used to design importance sampling changes of measure for stochastic recursive equations. Analogous to what has been done for large deviations subsolution-based… read more here.
Keywords: stochastic recursive; moderate deviations; importance sampling; based importance ... See more keywords
Importance sampling of heavy-tailed iterated random functions
Sign Up to like & getrecommendations! Published in 2018 at "Advances in Applied Probability"
DOI: 10.1017/apr.2018.37
Abstract: Abstract We consider the stationary solution Z of the Markov chain {Zn}n∈ℕ defined by Zn+1=ψn+1(Zn), where {ψn}n∈ℕ is a sequence of independent and identically distributed random Lipschitz functions. We estimate the probability of the event… read more here.
Keywords: tailed iterated; heavy tailed; sampling heavy; random ... See more keywords