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Bayesian multilevel models in R using brms| old_uid | 18424 |
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| title | Bayesian multilevel models in R using brms |
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| start_date | 2020/10/01 |
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| schedule | 14h |
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| online | no |
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| location_info | salle Mont-Blanc |
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| summary | Bayesian multilevel models are increasingly used to overcome the limitations of frequentist and single-levels approaches in the analysis of complex structured data (e.g., repeated measures, meta-analysis). During this talk, I will briefly introduce the foundations of Bayesian inference and motivate the use of multilevel models for the analysis of experimental data in the speech sciences. I will then show how Bayesian multilevel models can be fitted using the probabilistic programming language Stan and the brms front-end R package (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. |
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| responsibles | Meyer, Ito |
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