Results 71 to 80 of about 40,789 (244)
Large-scale Assessments in EducationMediation analysis in large-scale assessments often involves a multilevel structure, where students are nested within classrooms or schools. In such a context, multilevel structural equation modeling (MSEM) provides a flexible framework for estimating ...
Yue Li, Fan Jia
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Multi-level Monte Carlo computation of the hadronic vacuum polarization contribution to (gμ − 2)
Physics Letters B, 2021 The hadronic contribution to the muon anomalous magnetic moment aμ=(gμ−2)/2 has to be determined at the per-mille level for the Standard Model prediction to match the expected final uncertainty from the ongoing E989 experiment.
Mattia Dalla Brida +3 more
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Multilevel Monte Carlo Methods [PDF]
, 2001 We study Monte Carlo approximations to high dimensional parameter dependent integrals. We survey the multilevel variance reduction technique introduced by the author in [4] and present extensions and new developments of it. The tools needed for the convergence analysis of vector-valued Monte Carlo methods are discussed, as well.
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Advanced Intelligent Discovery, EarlyView.This work establishes a correlation between solvent properties and the charge transport performance of solution‐processed organic thin films through interpretable machine learning. Strong dispersion interactions (δD), moderate hydrogen bonding (δH), closely matching and compatible with the solute (quadruple thiophene), and a small molar volume (MolVol)
Tianhao Tan, Lian Duan, Dong Wang
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Multilevel Monte Carlo methods for applications in finance
, 2012 Since Giles introduced the multilevel Monte Carlo path simulation method [18], there has been rapid development of the technique for a variety of applications in computational finance.
Giles, Mike, Szpruch, Lukasz
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Advanced Intelligent Discovery, EarlyView.This article outlines how artificial intelligence could reshape the design of next‐generation transistors as traditional scaling reaches its limits. It discusses emerging roles of machine learning across materials selection, device modeling, and fabrication processes, and highlights hierarchical reinforcement learning as a promising framework for ...
Shoubhanik Nath +4 more
wiley +1 more source
Austrian Journal of Statistics, 2022 The multilevel and poststratification approach is commonly used to draw valid inference from (non-probabilistic) surveys. This Bayesian approach includes varying regression coefficients for which prior distributions of their variance parameter must be ...
Christian Bruch, Barbara Felderer
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Multilevel Sequential Monte Carlo with Dimension-Independent Likelihood-Informed Proposals [PDF]
, 2017 In this article we develop a new sequential Monte Carlo (SMC) method for multilevel (ML) Monte Carlo estimation. In particular, the method can be used to estimate expectations with respect to a target probability distribution over an infinite-dimensional
Beskos, Alexandros +4 more
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A Flexible and Energy‐Efficient Compute‐in‐Memory Accelerator for Kolmogorov–Arnold Networks
Advanced Intelligent Systems, EarlyView.This article presents KA‐CIM, a compute‐in‐memory accelerator for Kolmogorov–Arnold Networks (KANs). It enables flexible and efficient computation of arbitrary nonlinear functions through cross‐layer co‐optimization from algorithm to device. KA‐CIM surpasses CPU, ASIC, VMM‐CIM, and prior KAN accelerators by 1–3 orders of magnitude in energy‐delay ...
Chirag Sudarshan +6 more
wiley +1 more source
A Mixed PDE/Monte Carlo approach as an efficient way to price under high-dimensional systems [PDF]
, 2013 We propose to price derivatives modelled by multi-dimensional systems of stochastic di fferential\ud equations using a mixed PDE/Monte Carlo approach.
Ang, Xing Xian
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