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Why R? Webinar 035 - brms: Bayesian Regression Models using Stan

Wydarzenie:
Why R? Webinar 035 - Bayesian Regression Models using Stan
Typ wydarzenia:
Webinar
Kategoria:
IT
Tematyka:
Data:
25.02.2021 (czwartek)
Godzina:
20:00
Język:
angielski
Wstęp:
Bezpłatne
Miasto:
Miejsce:
Online Webinar
Adres:
On your computer
Prelegenci:
Opis:

- stream https://youtu.be/OUyB4kiJcWE


- speaker Paul Buerkner https://paul-buerkner.github.io/) Cluster of Excellence SimTech, University of Stuttgart, Germany

- webinars http://whyr.pl/webinars/

- subscribe http://whyr.pl/subscribe/

- slack http://whyr.pl/slack/

- meetup http://tiny.cc/WarsawRUG


# Description


The brms package provides an interface to fit Bayesian generalized (non-)linear multivariate multilevel models using Stan, a C++ package for performing full Bayesian inference; see https://mc-stan.org/. The formula syntax is very similar to that of the lme4 package to provide a familiar and simple interface for performing regression analyses. A wide range of response distributions are supported, allowing users to fit – among others – linear, robust linear, count data, survival, response times, ordinal, zero-inflated, and even self-defined mixture models all in a multilevel context. Further modeling options include non-linear and smooth terms, auto-correlation structures, censored data, missing value imputation, and quite a few more. In addition, all parameters of the response distribution can be predicted in order to perform distributional regression. Multivariate models, i.e., models with multiple response variables, can be fit as well. Prior specifications are flexible and explicitly encourage users to apply prior distributions that reflect their beliefs. Model fits can easily be assessed and compared with posterior predictive checks, cross-validation, and Bayes factors.


# Speaker


Paul is a statistician currently working as an Independent Junior Research Group Leader at the Cluster of Excellence SimTech at the University of Stuttgart (Germany). He is the author of the R package brms and member of the Stan Development Team. Previously, he studied Psychology and Mathematics at the Universities of Münster and Hagen (Germany) and did his PhD in Münster about optimal design and Bayesian data analysis. He has also worked as a Postdoctoral researcher at the Department of Computer Science at Aalto University (Finland).


# Sponsors #JumpingRivers


The event will be sponsored by Jumping Rivers.


Jumping Rivers is an analytics company whose passion is data and machine learning. https://www.jumpingrivers.com/


twitter: https://twitter.com/jumping_uk

linkedin: https://www.linkedin.com/company/jumping-rivers-ltd/


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