Materials for the Workshop on Bayesian Semiparametric Distributional Regression in Florence, February 2025.
This workshop provides an overview of key statistical modeling approaches, starting with the principles of Bayesian inference, followed by semiparametric regression with structured additive predictors, and concluding with distributional regression models. Topics include methods such as MCMC simulations, spline smoothing, and generalized additive models for location, scale, and shape, with a focus on both theory and application.
- Principles of Bayesian Inference
- Markov Chain Monte Carlo Simulations
- Monitoring Mixing and Convergence
- Posterior Summaries
- Penalized Spline Smoothing
- A Generic Basis Function Framework
- Spatial Smoothing
- Random Effects Models
- Hyperpriors for the Smoothing Parameter
- Interactions and Identification
- Generalized Additive Models for Location, Scale and Shape
- Applications with Continuous, Discrete and Multivariate Responses
- Other Frameworks for Distributional Regression
- Please bring a laptop, so that you can actively work on the tutorials.
- On your laptop, you should have a recent version of R and RStudio installed.
- Prior programming experience:
- You will need experience in R for the practicals of Day 1.
- For the practicals of the Days 2 and 3, wee offer two versions:
- R version: Relies on the R package
bamlss. This package is a great starting point for users familiar with R who want to apply Bayesian Semiparametric Distribtional Regression models. - Python version: Relies on the Python library
liesel. This library is our own development, and is our method of choice for developing new Bayesian models. Beware that the initial learning curve is higher than forbamlss, and that Liesel offers less convenience functionality out of the box.
- If you want to work on the Python exercises, we recommend to use Google Colab. Of course, you are free to use your own local Python installation, in which case we recommend the use of Jupyter notebooks. For details, see below.
09:00 - 10:30 Lecture 1
10:30 - 11:00 Coffee Break
11:00 - 12:30 Lecture 2
12:30 - 14:00 Lunch Break
14:00 - 15:30 Practicals 1
15:30 - 16:00 Coffee Break
16:00 - 17:00 Practicals 2
09:00 - 10:30 Lecture 1
10:30 - 11:00 Coffee Break
11:00 - 12:30 Lecture 2
12:30 - 14:00 Lunch Break
14:00 - 15:30 Practicals 1
15:30 - 16:00 Coffee Break
16:00 - 17:00 Practicals 2
09:00 - 10:30 Lecture 1
10:30 - 11:00 Coffee Break
11:00 - 12:30 Lecture 2
12:30 - 14:00 Lunch Break
14:00 - 15:30 Practicals 1
15:30 - 16:00 Coffee Break
16:00 - 17:00 Practicals 2
To run Liesel on Google colab, you can install it (and some dependencies) with the following command:
!apt install libgraphviz-dev
!pip install liesel pygraphviz plotnine
You can install the latest Liesel release via pip:
pip install liesel
We strongly recommend that you also install pygraphviz, as it is important for
plotting Liesel models. Installation for pygraphviz differs by operating system, please
refer to the documention: PyGraphviz installation
For general-purpose plotting, we like to use plotnine, which brings ggplot2-like
syntax to Python:
pip install plotnine
- We are happy to support your work with Bayesian Distributional Regression! Please feel free to approach us with questions.
- You can contact the Liesel development team via [FILL PLACEHOLDER]
- Please also feel encouraged to ask questions on our discussion board: https://github.com/liesel-devs/liesel/discussions
- Liesel documentation: https://docs.liesel-project.org/en/latest/