**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!

## ABC gas

Posted in pictures, Running, Travel with tags ABC, ABC in Helsinki, brands, Finland, gas station, Helsinki, Munkkiniemen, tramways on August 9, 2017 by xi'an## European statistics in Finland [EMS17]

Posted in Books, pictures, Running, Statistics, Travel, University life with tags ABC, AISTATS 2016, Amazon, AMIS, Bayesian optimisation, deterministic mixtures, EMS 2017, Europe, European Meeting of Statisticians, exact Monte Carlo, Helsinki, INLA, particle filters, probabilistic numerics, University of Helsinki on August 2, 2017 by xi'an**W**hile this European meeting of statisticians had a wide range of talks and topics, I found it to be more low key than the previous one I attended in Budapest, maybe because there was hardly any talk there in applied probability. (But there were some sessions in mathematical statistics and Mark Girolami gave a great entry to differential geometry and MCMC, in the spirit of his 2010 discussion paper. Using our recent trip to Montréal as an example of geodesic!) In the Bayesian software session [organised by Aki Vetahri], Javier Gonzáles gave a very neat introduction to Bayesian optimisation: he showed how optimisation can be turned into Bayesian inference or more specifically as a Bayesian decision problem using a loss function related to the problem of interest. The point in following a Bayesian path [or probabilist numerics] is to reduce uncertainty by the medium of prior measures on functions, although resorting [as usual] to Gaussian processes whose arbitrariness I somehow dislike within the infinity of priors (aka stochastic processes) on functions! One of his strong arguments was that the approach includes the possibility for design in picking the next observation point (as done in some ABC papers of Michael Guttman and co-authors, incl. the following talk at EMS 2017) but again the devil may be in the implementation when looking at minimising an objective function… The notion of the myopia of optimisation techniques was another good point: only looking one step ahead in the future diminishes the returns of the optimisation and an alternative presented at AISTATS 2016 [that I do not remember seeing in Càdiz] goes against this myopia.

Umberto Piccini also gave a talk on exploiting synthetic likelihoods in a Bayesian fashion (in connection with the talk he gave last year at MCqMC 2016). I wondered at the use of INLA for this Gaussian representation, as well as at the impact of the parameterisation of the summary statistics. And the session organised by Jean-Michel involved Jimmy Olson, Murray Pollock (Warwick) and myself, with great talks from both other speakers, on PaRIS and PaRISian algorithms by Jimmy, and on a wide range of exact simulation methods of continuous time processes by Murray, both managing to convey the intuition behind their results and avoiding the massive mathematics at work there. By comparison, I must have been quite unclear during my talk since someone interrupted me about how Owen & Zhou (2000) justified their deterministic mixture importance sampling representation. And then left when I could not make sense of his questions [or because it was lunchtime already].