Project owners: Richard Gao & Michael Deistler
Amortized neural Generalized Bayesian Inference for SBI applications: using neural network-based regression and density estimation to do generalized Bayesian inference, i.e., using distance functions as pseudo-likelihood functions.
pip install -e . to run setup.
pip install -e packages/sbi/ to install local version of sbi.
- Run notebooks in
paper/fig1/01_generate_figure.ipynb - Convert the svg via
invoke convert 1 - Upload to overleaf
- Make x_o:
cd gbi/benchmark/tasks/,python generate_xo.py 'gaussian_mixture' 10 - Generate ground-truth GBI posterior samples from x_os:
cd gbi/benchmark/generate_gt/,python run_gaussian_mixture.py -m task.xo_index=0,1,2,3,4,5,6,7,8,9 task.is_specified='specified','misspecified' task.is_known='known','unknown' task.beta=2.,10.,50. task.name=gaussian_mixture - Train algorithms (can be done separately from step 2):
cd gbi/benchmark/run_algorithms/,python run_training.py -m task.name=gaussian_mixture algorithm=NPE,NLE,GBI - Do inference with trained algorithms:
cd gbi/benchmark/run_algorithms/,python run_inference.py -m algorithm=GBI trained_inference_datetime='$YYYY_MM_DD__hh_mm_ss' task.name='gaussian_mixture' task.xo_index=0,1,2,3,4,5,6,7,8,9 task.is_specified=specified,misspecified task.is_known=known,unknown task.beta=2.,10.,50.. Note for NPE and NLE there is no need to sweep overbeta.