I (Aki) am looking for a post-doc to work on developing Bayesian workflow tools at Aalto University, Finland, and Finnish Center for Artificial Intelligence, in collaboration with Andrew, Dan Simpson, Paul Bürkner, Lauren Kennedy, Måns Magnusson, Stan developers, ArviZ developers, and others. The topic is related to many ideas discussed in Bayesian Workflow paper. You […]

## Post-stratified longitudinal item response model for trust in state institutions in Europe

This is a guest post by Marta Kołczyńska: Paul, Lauren, Aki, and I (Marta) wrote a preprint where we estimate trends in political trust in European countries between 1989 and 2019 based on cross-national survey data. This paper started from the following question: How to estimate country-year levels of political trust with data from surveys […]

## More limitations of cross-validation and actionable recommendations

This post is by Aki. Tuomas Sivula, Måns Magnusson, and I (Aki) have a new preprint paper that analyzes one of the limitations of cross-validation Uncertainty in Bayesian Leave-One-Out Cross-Validation Based Model Comparison. Normal distribution has been used to present the uncertainty in cross-validation for a single model and in model comparison at least since […]

## Aki’s talk about reference models in model selection in Laplace’s demon series

I (Aki) talk about reference models in model selection in Laplace’s demon series 24 June 15UTC (Finland 18, Paris 17, New York 11). See the seminar series website for a registration link, the schedule for other talks, and the list of the recorded talks. The short summary: 1) Why a bigger model helps inference for […]

## Several post-doc positions in probabilistic programming etc. in Finland

There are several open post-doc positions in Aalto and University of Helsinki in 1. probabilistic programming, 2. simulator-based inference, 3. data-efficient deep learning, 4. privacy preserving and secure methods, 5. interactive AI. All these research programs are connected and collaborating. I (Aki) am the coordinator for the project 1 and contributor in the others. Overall […]

## StanCon 2018 Helsinki talk slides, notebooks and code online

StanCon 2018 Helsinki talk slides, notebooks and code have been available for some time in StanCon talks repository, but it seems we forgot to announce this. The StanCon 2018 Helsinki talk list includes also links to videos. StanCon’s version of conference proceedings is a collection of contributed talks based on interactive notebooks. Every submission is […]

## Postdocs and Research fellows for combining probabilistic programming, simulators and interactive AI

Here’s a great opportunity for those interested in probabilistic programming and workflows for Bayesian data analysis: We (including me, Aki) are looking for outstanding postdoctoral researchers and research fellows to work for a new exciting project in the crossroads of probabilistic programming, simulator-based inference and user interfaces. You will have an opportunity to work with […]

## Postdoc position: Stan and composite mechanistic and data-driven models of cellular metabolism

Very cool project and possibility to work 3 years developing Stan and collaborating with me (Aki) and other Stan development team. Deadline for applications is 22 October. Quantitative Modelling of Cell Metabolism (QMCM) group headed by Professor Lars Keld Nielsen at DTU, Copenhagen, is looking for experienced Bayesian statistician for a postdoc position. Group specializes […]

## StanCon 2018 Helsinki tutorial videos online

StanCon 2018 Helsinki tutorial videos are now online at Stan YouTube channel List of tutorials at StanCon 2018 Helsinki Basics of Bayesian inference and Stan, parts 1 + 2, Jonah Gabry & Lauren Kennedy Hierarchical models, parts 1 + 2, Ben Goodrich Stan C++ development: Adding a new function to Stan, parts 1 + 2, […]

## When LOO and other cross-validation approaches are valid

Introduction Zacco asked in Stan discourse whether leave-one-out (LOO) cross-validation is valid for phylogenetic models. He also referred to Dan’s excellent blog post which mentioned iid assumption. Instead of iid it would be better to talk about exchangeability assumption, but I (Aki) got a bit lost in my discourse answer (so don’t bother to go […]

## Parsimonious principle vs integration over all uncertainties

tl;dr If you have bad models, bad priors or bad inference choose the simplest possible model. If you have good models, good priors, good inference, use the most elaborate model for predictions. To make interpretation easier you may use a smaller model with similar predictive performance as the most elaborate model. Merijn Mestdagh emailed me […]

## Comments on Limitations of Bayesian Leave-One-Out Cross-Validation for Model Selection

There is a recent pre-print Limitations of Bayesian Leave-One-Out Cross-Validation for Model Selection by Quentin Gronau and Eric-Jan Wagenmakers. Wagenmakers asked for comments and so here are my comments. Short version: They report a known limitation of LOO when it’s used in a non-recommended way for model selection. They report that their experiments show that […]

## Aki’s favorite scientific books (so far)

A month ago I (Aki) started a series of tweets about “scientific books which have had big influence on me…”. They are partially in time order, but I can’t remember the exact order. I may have forgotten some, and some stretched the original idea, but I can recommend all of them. I have collected all […]

## The curse of dimensionality and finite vs. asymptotic convergence results

Related to our (Aki, Andrew, Jonah) Pareto smoothed importance sampling paper I (Aki) received a few times a comment that why bother with Pareto smoothing as you can always choose the proposal distribution so that importance ratios are bounded and then central limit theorem holds. The curse of dimensionality here is that the papers they […]

## Stacking and multiverse

It’s a coincidence that there is another multiverse posting today. Recently Tim Disher asked in Stan discussion forum a question “Multiverse analysis – concatenating posteriors?” Tim refers to a paper “Increasing Transparency Through a Multiverse Analysis” by Sara Steegen, Francis Tuerlinckx, Andrew Gelman, and Wolf Vanpaemel. The abstract says Empirical research inevitably includes constructing a […]

## StanCon 2018 Helsinki, 29-31 August 2018

Photo (c) Visit Helsinki / Jussi Hellsten StanCon 2018 Asilomar was so much fun that we are organizing StanCon 2018 Helsinki August 29-31, 2018 at Aalto University, Helsinki, Finland (location chosen using antithetic sampling). Full information is available at StanCon 2018 Helsinki website Summary of the information What: One day of tutorials and two days […]

## Custom Distribution Solutions

I (Aki) recently made a case study that demonstrates how to implement user defined probability functions in Stan language (case study, git repo). As an example I use the generalized Pareto distribution (GPD) to model extreme values of geomagnetic storm data from the World Data Center for Geomagnetism. Stan has had support for user defined […]

## Postdoc in Finland and NY to work on probabilistic inference and Stan!

I (Aki) got 2 year funding to hire a postdoc to work on validation of probabilistic inference approaches and model selection in Stan. Work would be done with Stan team in Aalto, Helsinki and Columbia, New York. We probably have PhD positions, too. The funding is part of the joint project with Antti Honkela and […]

## Tenure-Track or Tenured Prof. in Machine Learning in Aalto, Finland

This job advertisement for a position in Aalto, Finland, is by Aki We are looking for a professor to either further strengthen our strong research fields, with keywords including statistical machine learning, probabilistic modelling, Bayesian inference, kernel methods, computational statistics, or complementing them with deep learning. Collaboration with other fields is welcome, with local opportunities […]

## Stacking, pseudo-BMA, and AIC type weights for combining Bayesian predictive distributions

This post is by Aki. We have often been asked in the Stan user forum how to do model combination for Stan models. Bayesian model averaging (BMA) by computing marginal likelihoods is challenging in theory and even more challenging in practice using only the MCMC samples obtained from the full model posteriors. Some users have […]