Results 21 to 30 of about 545,959 (267)
Multilevel multifidelity Monte Carlo methods for assessing uncertainty in coastal flooding [PDF]
Natural Hazards and Earth System Sciences, 2022 When choosing an appropriate hydrodynamic model, there is always a compromise between accuracy and computational cost, with high-fidelity models being more expensive than low-fidelity ones.
M. C. A. Clare +6 more
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Bayesian model selection for multilevel models using integrated likelihoods.
PLoS ONE, 2023 Multilevel linear models allow flexible statistical modelling of complex data with different levels of stratification. Identifying the most appropriate model from the large set of possible candidates is a challenging problem. In the Bayesian setting, the
Tom Edinburgh, Ari Ercole, Stephen Eglen
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Nested Multilevel Monte Carlo with Biased and Antithetic Sampling [PDF]
arXiv.org, 2023 We consider the problem of estimating a nested structure of two expectations taking the form $U_0 = E[\max\{U_1(Y), \pi(Y)\}]$, where $U_1(Y) = E[X\ |\ Y]$. Terms of this form arise in financial risk estimation and option pricing.
A. Haji-Ali, Jonathan Spence
semanticscholar +1 more source
Advanced Multilevel Monte Carlo Methods [PDF]
International Statistical Review, 2020 SummaryThis article reviews the application of some advanced Monte Carlo techniques in the context of multilevel Monte Carlo (MLMC). MLMC is a strategy employed to compute expectations, which can be biassed in some sense, for instance, by using the discretization of an associated probability law.
Ajay Jasra, Kody Law, Carina Suciu
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Optimizing shift selection in multilevel Monte Carlo for disconnected diagrams in lattice QCD [PDF]
Computer Physics Communications, 2022 The calculation of disconnected diagram contributions to physical signals is a computationally expensive task in Lattice QCD. To extract the physical signal, the trace of the inverse Lattice Dirac operator, a large sparse matrix, must be stochastically ...
Travis Whyte +3 more
semanticscholar +1 more source
Accelerated Simulation of Boltzmann-BGK Equations near the Diffusive Limit with Asymptotic-Preserving Multilevel Monte Carlo [PDF]
SIAM Journal on Scientific Computing, 2022 Kinetic equations model the position-velocity distribution of particles subject to transport and collision effects. Under a diffusive scaling, these combined effects converge to a diffusion equation for the position density in the limit of an infinite ...
Emil Løvbak, G. Samaey
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Goal-Oriented Adaptive Finite Element Multilevel Monte Carlo with Convergence Rates [PDF]
Computer Methods in Applied Mechanics and Engineering, 2022 We present an adaptive multilevel Monte Carlo (AMLMC) algorithm for approximating deterministic, real-valued, bounded linear functionals that depend on the solution of a linear elliptic PDE with a lognormal diffusivity coefficient and geometric ...
Joakim Beck +3 more
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Multilevel Monte Carlo Metamodeling [PDF]
Operations Research, 2013 Approximating the function that maps the input parameters of the simulation model to the expectation of the simulation output is an important and challenging problem in stochastic simulation metamodeling. Because an expectation is an integral, this function approximation problem can be seen as parametric integration—approximating the function that ...
Imry Rosenbaum, Jeremy Staum
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Multilevel Monte Carlo with Surrogate Models for Resource Adequacy Assessment [PDF]
IEEE International Conference on Probabilistic Methods Applied to Power Systems, 2022 Monte Carlo simulation is often used for the reliability assessment of power systems, but it converges slowly when the system is complex. Multilevel Monte Carlo (MLMC) can be applied to speed up computation without compromises on model complexity and ...
Ensieh Sharifnia, Simon Tindemans
semanticscholar +1 more source
Multilevel sequential Monte Carlo samplers [PDF]
Stochastic Processes and their Applications, 2017 In this article we consider the approximation of expectations w.r.t. probability distributions associated to the solution of partial differential equations (PDEs); this scenario appears routinely in Bayesian inverse problems. In practice, one often has to solve the associated PDE numerically, using, for instance finite element methods and leading to a ...
Beskos, Alexandros +4 more
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