Archive for Monte Carlo Statistical Methods
celebrating the career of Adrian Raftery
Posted in Books, Mountains, Statistics, Travel, University life with tags ABCDE, Adrian Raftery, applied statistics, Bayesian statistics, computational Bayesian methods, Environmental Statistics, Ireland, JSM 2024, Monte Carlo Statistical Methods, Oregon, Pacific North West, Paris 6, Portland, Seattle, statistical demography, United Nations, University of Washington, USA, UW, Washington State on July 28, 2024 by xi'an
Xuriouser & Xuriouser
Posted in Books, Statistics with tags accept, Bayesian computational methods, cross validated, Metropolis-Hastings algorithms, Monte Carlo Statistical Methods, normalising constant, principle of proportionality, screenshot, simulation on May 8, 2024 by xi'an
mostly MC [April]
Posted in Books, Kids, Statistics, University life with tags #ERCSyG, Bayesian computational methods, Bayesian inference, denoising, generative models, Institut PR[AI]RIE, JMLR, machine learning, Markov chain Monte Carlo, MCMC, Monte Carlo methods, Monte Carlo Statistical Methods, mostly Monte Carlo seminar, multimodal target, Ocean, optimization, Paris, PariSanté campus, PSC, sampling, score-based generative models, seminar, simulation, stochastic diffusions, stochastic localization, variational autoencoders on April 5, 2024 by xi'an
[very] simple rejection Monte Carlo
Posted in Books, pictures, R, University life with tags accept-reject algorithm, arXiv, Brussels, dominating measure, history of Monte Carlo, John von Neumann, Monte Carlo Statistical Methods, Non-Uniform Random Variate Generation, pseudo-random generator on March 29, 2024 by xi'an
“In recent years, the Rejection Monte Carlo (RMC) algorithm has emerged sporadically in literature under alternative names such as screening sampling or reject-accept sampling algorithms”
First, I was intrigued enough by a new arXival spotted in the Thalys train from Brussels to take a deeper look at it, but soon realised there was nothing of substance in the paper. Which solely recalls the fundamental of (accept-)reject algorithms, invented in the early days of computer simulation by von Neumann (even though the preprint refers to much more recent publications). Without providing the average acceptance probability as being equal to the inverse of the bounding constant [independently of the dimension of the random variable] and no mention of The Bible either… But with a standard depiction of accepted vs rejected points as uniformly dispersed on the subgraph of the proposal (as in the above taken from our very own Monte Carlo statistical Methods). Funnily enough, the most basic rejection algorithm, that is, the one based on a uniform sampling from a bounding (hyper)box is illustrated for a Normal target, although the latter has infinite support. And the paper seems to conclude on the appeal of using uniform proposals over bounding boxes, even though the increasing inefficiency against the dimension is well-known. A very simple rejection then, indeed!
on the edge and online!
Posted in Books, Statistics, Travel, University life with tags advanced Monte Carlo methods, antithetic sampling, convergence, efficient importance sampling, MCMC, Monte Carlo Statistical Methods, open access, pseudo-random generators, simulation, Statistical Science, Università Ca' Foscari Venezia, Venice on March 11, 2024 by xi'an
