I would like to highlight two resources that, in my humble opinion as ISBA Social Media Manager, remain under-recognized yet immensely valuable:
ISBA Webinars on Bayesian Analysis Articles (2019–present).This webpage gathers an exceptional collection of webinars discussing Bayesian Analysis articles since 2019. For anyone curious about the frontiers of Bayesian statistics, this series brings together cutting-edge research from world-class experts. The talks span topics such as model uncertainty and missing data, new perspectives on stick-breaking models, sparse Bayesian factor analysis, and advances in causal inference under model mis-specification. Other contributions cover nonparametric priors, spatio-temporal modeling of Arctic sea ice, Bayesian regression trees for causal inference, and much more.The next BA webinar will focus on the paper “Model Uncertainty and Missing Data: An Objective Bayesian Perspective” by G. García-Donato, M. Eugenia Castellanos, S. Cabras, A. Quirós, and A. Forte. There will be four invited discussants: M. Clyde, M. Ferreira, A. Ly, and J. Rubio. It is scheduled for November 5, 2025 (4:00 PM UTC | 11:00 AM EST | 5:00 PM CET). Registration will be announced later on this webpage.ISBA YouTube Channel.All recorded webinar videos are available on the ISBA YouTube channel. Beyond the webinars, the channel hosts curated playlists from ISBA World Meetings (2012, 2016, 2018, 2021, 2022, 2024), specialized workshops and seminars (ABI, BNP, BayesComp), as well as content from ISBA Sections (j-ISBA, BNP, BioPharma, Industrial).These resources deserve broad visibility. I warmly encourage you to explore them, share them within your networks, and let us know your feedback.— Julyan, on behalf of the ISBA Social Media team
Archive for Helsinki
the most important statistical ideas of the past 50 years
Posted in Books, pictures, Statistics, Travel with tags ABC, applied statistics, Bayesian Analysis, Bayesian nonparametrics, Bayesian optimization, big data, Biometrika, bootstrap, divide & conquer, EDA, Helsinki, list, machine learning, reinforcement learning, replication crisis, robustness, statistical computing, theory of statistics on January 10, 2020 by xi'an
Aki and Andrew are celebrating the New Year in advance by composing a list of the most important statistics ideas occurring (roughly) since they were born (or since Fisher died)! Like
- substitution of computing for mathematical analysis (incl. bootstrap)
- fitting a model with a large number of parameters, using some regularization procedure to get stable estimates and good predictions (e.g., Gaussian processes, neural networks, generative adversarial networks, variational autoencoders)
- multilevel or hierarchical modelling (incl. Bayesian inference)
- advances in statistical algorithms for efficient computing (with a long list of innovations since 1970, including ABC!), pointing out that a large fraction was of the divide & conquer flavour (in connection with large—if not necessarily Big—data)
- statistical decision analysis (e.g., Bayesian optimization and reinforcement learning, getting beyond classical experimental design )
- robustness (under partial specification, misspecification or in the M-open world)
- EDA à la Tukey and statistical graphics (and R!)
- causal inference (via counterfactuals)
Now, had I been painfully arm-bent into coming up with such a list, it would have certainly been shorter, for lack of opinion about some of these directions (even the Biometrika deputeditoship has certainly helped in reassessing the popularity of different branches!), and I would have have presumably been biased towards Bayes as well as more mathematical flavours. Hence objecting to the witty comment that “theoretical statistics is the theory of applied statistics”(p.10) and including Ghosal and van der Vaart (2017) as a major reference. Also bemoaning the lack of long-term structure and theoretical support of a branch of the machine-learning literature.
Maybe also more space and analysis could have been spent on “debates remain regarding appropriate use and interpretation of statistical methods” (p.11) in that a major difficulty with the latest in data science is not so much the method(s) as the data on which they are based, which in a large fraction of the cases, is not representative and is poorly if at all corrected for this bias. The “replication crisis” is thus only one (tiny) aspect of the challenge.
ABC in Svalbard, April 12-13 2021
Posted in Mountains, pictures, Statistics, Travel, University life with tags 80th parallel North, ABC, ABC in Helsinki, ABC in Svalbard, ABCruise, Approximate Bayesian computation, Arctic, boat, cruise, Helsinki, Iceland, Longyearbyen, midnight sun, Norway, Spitzberg, Stockholm, Svalbard, workshop on November 4, 2019 by xi'an
deep and embarrassingly parallel MCMC
Posted in Books, pictures, Statistics with tags arXiv, banana, consensus Monte Carlo, curse of dimensionality, deep learning, embarrassingly parallel MCMC, Helsinki, University of Helsinki on April 9, 2019 by xi'an
Diego Mesquita, Paul Blomstedt, and Samuel Kaski (from Helsinki, like the above picture) just arXived a paper on embarrassingly parallel MCMC. Following a series of papers discussed on this ‘og in the past. They use a deep learning approach of Dinh et al. (2017) to the computation of the probability density of a convoluted and non-volume-preserving transform of a given random variable to turn multiple samples from sub-posteriors [corresponding to the k k-th roots of the true posterior] into a sample from the true posterior. If I understand correctly the argument [on page 4], the deep neural network provides a density estimate that apparently does better than traditional non-parametric density estimates. Maybe by being more efficient than a Parzen-Rosenblat estimator which is of order the number of simulations… For any value of θ, the estimate of the true target is the product of these estimates and for a value of θ simulated from one of the subposteriors an importance weight naturally ensues. However, for a one-dimensional transform of θ, h(θ), I would prefer estimating first the density of h(θ) for each sample and then construct an importance weight. If only to avoid the curse of dimension.
On various benchmarks, like the banana-shaped 2D target above, the proposed method (NAP) does better. Even in relatively high dimensions. Given that the overall computing times are not produced, with only the calibration that the same number of subsamples were produced for all methods, it would be interesting to test the same performances on even higher dimensions and larger population sizes.
a new rule for adaptive importance sampling
Posted in Books, Statistics with tags adaptive importance sampling, AMIS, empirical likelihood, Helsinki, MCMC, Monte Carlo integration, Monte Carlo Statistical Methods, multiple importance methods, pseudo-random generators, University of Warwick on March 5, 2019 by xi'anArt Owen and Yi Zhou have arXived a short paper on the combination of importance sampling estimators. Which connects somehow with the talk about multiple estimators I gave at ESM last year in Helsinki. And our earlier AMIS combination. The paper however makes two important assumptions to reach optimal weighting, which is inversely proportional to the variance:
- the estimators are uncorrelated if dependent;
- the variance of the k-th estimator is of order a (negative) power of k.
The later is puzzling when considering a series of estimators, in that k appears to act as a sample size (as in AMIS), the power is usually unknown but also there is no reason for the power to be the same for all estimators. The authors propose to use ½ as the default, both because this is the standard Monte Carlo rate and because the loss in variance is then minimal, being 12% larger.
As an aside, Art Owen also wrote an invited discussion “the unreasonable effectiveness of Monte Carlo” of ” Probabilistic Integration: A Role in Statistical Computation?” by François-Xavier Briol, Chris Oates, Mark Girolami (Warwick), Michael Osborne and Deni Sejdinovic, to appear in Statistical Science, discussion that contains a wealth of smart and enlightening remarks. Like the analogy between pseudo-random number generators [which work unreasonably well!] vs true random numbers and Bayesian numerical integration versus non-random functions. Or the role of advanced bootstrapping when assessing the variability of Monte Carlo estimates (citing a paper of his from 1992). Also pointing out at an intriguing MCMC paper by Michael Lavine and Jim Hodges to appear in The American Statistician.
