2021-07-26 –, Red
This workshop will introduce the recommended workflow for applied Bayesian data analysis by working through an example analysis together. We will start with the simplest non-trivial model and use increasingly sophisticated models to explain the properties of our data set based on model diagnostics. We will also give an overview of the different probabilistic programming packages in Julia and show where we have advantages over other languages such as Stan and Python.
We will give participants an intuition and diagnostics for the workhorse of modern Bayesian statistics: the Hamiltonian MCMC algorithm. Additionally, we will cover the following topics:
- modeling count data with Poisson regression
- modeling overdispersion with the negative Binomial model
- hierarchical modeling
- modeling time varying effects with autoregressive models and Gaussian processes
We will conclude the workshop by showcasing future potential and features that are not currently available elsewhere such as Bayesian neural ODEs and symbolic optimization of Bayesian models.
A Biostatistics PhD student from the University of Oslo. I'm working on combining computer simulations with Bayesian statistical models. I have been a big fan of Julia since version 0.4. My research interests are Bayesian statistics, symbolic computing and applied category theory.