Bayes in Grenoble: Ladislas Nalborczyk
on the November 28, 2019
November 28, at 4:00 pm
Ladislas Nalborczyk from LPNC will talk about "Bayesian statistics in R". The event will take place on November, 28 at 4:00 pm at IMAG building.
Bayes in Grenoble is a new reading group on Bayesian statistical methods. The purpose of this group is to gather the Grenoble Bayesian community on a monthly basis around noteworthy papers. Those can equally focus on theory, methods, learning, applications, computations, etc, and can be seminal papers as well as recent preprints, as soon as they relate to Bayes.
The reading group is organised by Julyan Arbel and Florence Forbes. Feel free to contact them if you wish to attend/be added to the mailing list and/or give a talk. https://sites.google.com/view/bigseminar/accueil
React on social media: #BIGseminar
Bayesian statistics in R
Abstract
Bayesian multilevel models are increasingly used to overcome the limitations of frequentist approaches in the analysis of complex structured data. During this session, I will briefly introduce the logic of Bayesian inference and motivate the use of multilevel modelling. I will then show how Bayesian multilevel models can be fitted using the probabilistic programming language Stan and the R package brms (Bürkner, 2016). The brms package allows fitting complex nonlinear multilevel (aka 'mixed-effects') models using an understandable high-level formula syntax. I will demonstrate the use of brms with some general examples and discuss model comparison tools available within the package. Prior experience with data manipulation and linear models in R will be helpful.
The reading group is organised by Julyan Arbel and Florence Forbes. Feel free to contact them if you wish to attend/be added to the mailing list and/or give a talk. https://sites.google.com/view/bigseminar/accueil
React on social media: #BIGseminar
28 November 2019, Ladislas Nalborczyk (LPNC)
Bayesian statistics in R
AbstractBayesian multilevel models are increasingly used to overcome the limitations of frequentist approaches in the analysis of complex structured data. During this session, I will briefly introduce the logic of Bayesian inference and motivate the use of multilevel modelling. I will then show how Bayesian multilevel models can be fitted using the probabilistic programming language Stan and the R package brms (Bürkner, 2016). The brms package allows fitting complex nonlinear multilevel (aka 'mixed-effects') models using an understandable high-level formula syntax. I will demonstrate the use of brms with some general examples and discuss model comparison tools available within the package. Prior experience with data manipulation and linear models in R will be helpful.
Published on February 25, 2020
Practical informations
Location
Meeting room 1
IMAG building