Titlebook: Advances in Modeling and Simulation; Festschrift for Pier Zdravko Botev,Alexander Keller,Bruno Tuffin Book 2022 The Editor(s) (if applicabl

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https://doi.org/10.1007/978-3-031-57627-0ate of the art, our simple algorithms neither require randomization, nor costly optimization, nor lookup tables. We analyze correlations of space-filling curves and low discrepancy sequences, and demonstrate the benefits of the new algorithms in a professional, massively parallel light transport simulation and rendering system.

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Book 2022mulation, modeling, and operations research over the last 40 years. This book contains 20 chapters written by collaborators and experts in the field who, by sharing their latest results, want to recognize the lasting impact of Pierre’s work in their research area. The breadth of the topics covered r

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Simona Giuratrabocchetta,Ivana Giannini and neural networks are difficult to design in the context of options. Variance reduction, achieved here by means of control variates, is a crucial tool to obtain reliable results at a reasonable cost.

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Topographic Anatomy in Proctologic Surgeryal with the same mean and variance, but we also consider gamma approximations in two variants, and show that in some cases these perform substantially better. Other algorithms are briefly surveyed and we sketch a new one for simulation of a tempered stable (CGMY) process with infinite variation.

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Adjustment to Empire, 1763–1770 non-negative functions. Typically, sampling from such posterior densities is only viable via approximate Markov chain Monte Carlo (MCMC). Finally, we propose a novel . sampler, which is a hybrid between rejection sampling and MCMC. The Reject-Regenerate sampler creates a Markov chain, whose states

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