Journal of Machine Learning Research1381 Submitted 11/11; Revised 10/13; Published 4/14 The No-U-Turn Sampler: Adaptively Setting Path Lengths in Hamiltonian Monte Carlo

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Monte Carlo Integration ˆ Monte Carlo integration methods are sampling methods, based on probability theory. ˆ They rely on trials to reveal information ˆ From an intuitive point of view, they rest on the central lim

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Spatial Averaging of van Genuchten Hydraulic Parameters for Steady-State Flow in Heterogeneous Soils: A Numerical Study Jianting Zhu and Binayak P. Mohanty* ABSTRACT factors. Kim and Strickeremployed Monte Carlo

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MONTE CARLO TRACK SIMULATIONS Posted onMichael Dingfelder East Carolina University Greenville, North Carolina

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Bridge Monte-Carlo: a novel approach to rare events of Gaussian processes S. Giordano\ , M. Gubinelli† , M. Pagano\ \ †

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Discrepancy theory and harmonic analysis Dmitriy Bilyk University of Minnesota “Uniform distribution and quasi-Monte Carlo methods”

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Implementation of a Component-By-Component Algorithm to Generate Small Low-Discrepancy Samples Benjamin Doerr, Michael Gnewuch, and Magnus Wahlstr¨om Abstract In [B. Doerr, M. Gnewuch, P. Kritzer, F. Pillichshammer. Mo

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• Quasirandomness
• Diophantine approximation
• Low-discrepancy sequence
• Numerical analysis
• Random number generation
• Randomized rounding
• Randomized algorithm
• Quasi-Monte Carlo method

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