This repo contains code to reproduce the experiments found in our manuscript
Ryan Giordano, Runjing Liu, Michael I. Jordan, Tamara Broderick. "Evaluating sensitivity to the Stick Breaking Prior in Bayesian Nonparametrics." https://arxiv.org/abs/2107.03584
We evaluated local sensitivity on three data analysis problems:
- a Gaussian mixture model of the canonical iris data set;
- a regression model of time-course gene expression data;
- and a topic model inferring population structure from genetic data.
To install the package used for all the models we consider:
pip install BNP_modeling
Dependencies include jax and the jax branch of paragami. These will be installed automatically with the command above.
Our iris experiments, mice experiments, and population genetics experiments are contained in the ./GMM_clustering/, GMM_regression_clustering, and ./structure/ folders, respectively. To install libraries specific to those experiments, run
pip install GMM_clustering
pip install GMM_regression_cluster
pip install structure
respectively.
The results presented in our main paper are produced entirely within Jupyter notebooks.
In each expreriment folder (./GMM_clustering/, GMM_regression_clustering and ./structure/),
the jupyter subfolder contains notebooks to reproduce our results.
For example, the ./GMM_clustering/jupyter/parametric_sensitivity.ipynb file reproduces the parametric sensitivity results for our GMM/iris epxeriment (Figure 2 in the paper).
