Archive for mostly Monte Carlo seminar
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
mostly M[ar]C[h]
Posted in Books, Kids, Statistics, University life with tags diffusions, generative modelling, generative models, Monte Carlo methods, Monte Carlo Statistical Methods, mostly Monte Carlo seminar, normalizing flow, optimal transport, optimization, Paris, PariSanté campus, push-forward distribution, seminar, stochastic diffusions, The Prairie Chair on February 27, 2024 by xi'an
mostly MC[bruary]
Posted in Books, Kids, Statistics, University life with tags completion, contour algorithm, Gibbs sampling, insufficiency, IQR, mad, manifold, MCMC, median, Monte Carlo methods, Monte Carlo Statistical Methods, mostly Monte Carlo seminar, Paris, PariSanté campus, sample median, seminar, The Prairie Chair on February 18, 2024 by xi'an
combining normalizing flows and QMC
Posted in Books, Kids, Statistics with tags Arianna Rosenbluth, arXiv, Illya Sobol, importance sampling, inverse cdf, John Halton, MCM 2023, Metropolis-Hastings algorithm, mostly Monte Carlo seminar, normalizing flow, Python, quasi-Monte Carlo methods, scrambling, Sobol sequences on January 23, 2024 by xi'an
My PhD student Charly Andral [presented at the mostly Monte Carlo seminar and] arXived a new preprint yesterday, on training a normalizing flow network as an importance sampler (as in Gabrié et al.) or an independent Metropolis proposal, and exploiting its invertibility to call quasi-Monte Carlo low discrepancy sequences to boost its efficiency. (Training the flow is not covered by the paper.) This extends the recent study of He et al. (which was presented at MCM 2023 in Paris) to the normalising flow setting. In the current experiments, the randomized QMC samples are computed using the SciPy package (Roy et al. 2023), where the Sobol’ sequence is based on Joe and Kuo (2008) and on Matouˇsek (1998) for the scrambling, and where the Halton sequence is based on Owen (2017). (No pure QMC was harmed in the process!) The flows are constructed using the package FlowMC. As expected the QMC version brings a significant improvement in the quality of the Monte Carlo approximations, for equivalent computing times, with however a rapid decrease in the efficiency as the dimension of the targetted distribution increases. On the other hand, the architecture of the flow demonstrates little relevance. And the type of RQMC sequence makes a difference, the advantage apparently going to a scrambled Sobol’ sequence.
mostly MC[nuary]
Posted in Books, Kids, Statistics, University life with tags Monte Carlo methods, Monte Carlo Statistical Methods, mostly Monte Carlo seminar, normalising flow, overdamped Langevin algorithm, Paris, PariSanté campus, quasi-Monte Carlo methods, seminar, The Prairie Chair on January 6, 2024 by xi'an
