rk-bfr-jump

A Bayesian framework for functional linear and logistic regression models, based on the theory of RKHS's, that leverages the capabilities of reversible jump MCMC samplers. An overview of the models and some experiments are available in Sections 2 and 4 of this article.

The reversible jump samplers use an implementation from the Eryn library, with speed improvements thanks to parallelization with numba. There are very minor tweaks to Eryn's source code; see Eryn_changes.md for a summary of the changes.

Code structure

• The folder rkbfr_jump contains the inference and prediction pipeline implemented. There is a utils folders inside with some utility files for simulation, experimentation and visualization.
• The folder reference_methods contains the implementation of some functional algorithms used for comparison.
• The experiments folder contains the numerical results from the experiments in the accompanying article.

The file experiments.py contains several experiments to test the performance of the models against other usual alternatives, functional or otherwise; a typical execution can be seen in the launch.sh file. Additionally, there are Jupyter notebooks that demonstrate the usage of the code.

Code developed for Python 3.11 (see requirements.txt or environment.yml).