**T**his post is a very preliminary announcement that Jukka Corander, Judith Rousseau and myself are planning an ABC in Svalbard workshop in 2021, on 12-13 April, following the “ABC in…” franchise that started in 2009 in Paris… It would be great to hear expressions of interest from potential participants towards scaling the booking accordingly. (While this is a sequel to the highly productive ABCruise of two years ago, between Helsinki and Stockholm, the meeting will take place in Longyearbyen, Svalbard, and participants will have to fly there from either Oslo or Tromsø, Norway, As boat cruises from Iceland or Greenland start later in the year. Note also that in mid-April, being 80⁰ North, Svalbard enjoys more than 18 hours of sunlight and that the average temperature last April was -3.9⁰C with a high of 4⁰C.) The scientific committee should be constituted very soon, but we already welcome proposals for sessions (and sponsoring, quite obviously!).

## Archive for Helsinki

## 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'an**A**rt 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.

## StanCon in Helsinki [29-31 Aug 2018]

Posted in Books, pictures, R, Statistics, Travel, University life with tags Aalto Science Institute, Baltic Sea, Bayesian Analysis, Bayesian conference, Finland, Helsinki, STAN, StanCon 2018, summer on March 7, 2018 by xi'anAs emailed to me by Aki Vehtari, the next StanCon will take place this summer in the wonderful city of Helsinki, at the end of August. On Aalto University Töölö Campus precisely. The list of speakers and tutorial teachers is available on the webpage. (The only “negative point” is that the conference does not include a Tuesday, the night of the transcendence 2 miles race!) Somewhat concluding this never-ending summer of Bayesian conferences!