**Slides.
**

This event has already taken place. Slides for the different talks are available below.

*Douwe van der Schaaf: Pointlogic
*

*Quentin F. Gronau: Generic Marginal Likelihood Estimation for Stan Models*

*Alina Ferecatu: Hierarchical Bayesian Analysis – From a binary logit to advanced models of bounded rationality*

**Date. **

Tuesday, 27 November 2018.

**Location. **

*Pointlogic Rotterdam*

Fascinatio Boulevard 290

3065 WB Rotterdam

The building is called “De Mark” and the meetup will take place on the 11th floor. A route description can be found here.

**Program.**

18:00 – 18:30. Pizzas (vegetarian and non-vegetarian) made available by Pointlogic

18:30 – 18:50. Welcome & presentation Pointlogic

18:50 – 19:35. First speaker: Quentin Gronau

19:35 – 19:50. Break

19:50 – 20:35. Second Speaker: Alina Ferecatu

20:35 – 21:30. Drinks

**Registration.**

You can register here.

**Sponsor.**

Pointlogic, 2 years ago acquired by multinational Nielsen, is a media research company. A part of Pointlogic’s business consists of analysing the effect of advertising on brand KPIs or TV program tune-in. For this purpose, Pointlogic uses (hierarchical) Bayesian regression models applied to respondent-level data. Stan is used to estimate the parameters of the Bayesian models. Currently, efforts are being made to run Stan in parallel in the cloud.

**Abstracts.**

*Generic Marginal Likelihood Estimation for Stan Models*

Quentin F. Gronau, Henrik Singmann, Eric-Jan Wagenmakers

In many statistical applications, the question of interest can be formulated as a model comparison between two or more possibly non-nested models. A Bayesian solution is to compute posterior model probabilities and Bayes factors. To obtain these quantities one needs to evaluate a model’s marginal likelihood which is often a high-dimensional integral that cannot be solved analytically. In this talk, we discuss bridge sampling, a sampling-based method for estimating the marginal likelihood, and we demonstrate how the bridgesampling R package can be used to obtain estimates of the marginal likelihood for models fitted in Stan in an automatic fashion.

*Hierarchical Bayesian Analysis – From a binary logit to advanced models of bounded rationality
*Alina Ferecatu

The presentation will walk you through how to iteratively develop and estimate a model using stan. The decision problem I will focus on is a behavioral task in which subjects learn the best outcome in experimental games. To illustrate the task, I will play a game involving a multiarmed bandit problem.

To model players’ behavior, I will start with a binary logit model, build it to a hierarchical model, and then introduce a quantal response specification which allows us to model players’ learning patterns. As a last step, I will build a customized likelihood function to assess decision makers’ learning behavior and other relevant behavioral patterns.

On the technical side, the first part of the presentation will be a step-by-step guide to estimating a simple Bayesian model in Stan. I will then show how to estimate hierarchical models with non-centered reparameterization. I will also briefly discuss model checking and model comparison using the loo package.