This repository is dedicated to experiments with Simulation-based Inference (SBI). It uses the algorithms implemented in the sbi package (Tejero-Cantero et al., 2020).
Take a look at the documentation..
The problem of inference is ubiquitous in modern science. In the past decades, many statistical tools have seen their use consolidated across different scientific fields, either through frequentist or Bayesian approaches.
In parallel, the design of more powerful and more efficient computing hardware has allowed for the design of high-fidelity simulations of physical systems with increasing complexity, allowing for the generation of synthetic data from them. However, performing inference from these simulators still remains challenging. In these contexts, the likelihood function is not explicitly calculated, and is instead implicitly defined by the data-generating process implemented by the simulator. The problem of performing inference with these systems has thus been named likelihood-free inference or simulation-based inference (hereafter SBI).
In the past few years, the development of more sophisticated Machine Learning techniques, in particular deep neural networks, and the production of specialized hardware for training has given new momentum to the field of SBI. For a high-level overview of the impact of these trends on the emergence of new methods, we refer the reader to this review paper by (Cranmer et al, 2019).
The number of scientific publications employing the SBI toolbox in their methodology has been rampant in the last couple of years. We highlight https://simulation-based-inference.org/, an automated aggregator of scientific articles related to the subject and spanning many different fields, such as statistics, economics, neuroscience, astrophysics and cosmology, epidemiology and ecology, and so on. Similarly, the Github repository https://github.com/smsharma/awesome-neural-sbi contains a curated list of publications, tutorials and software packages related to SBI.
Clone the repo and run
pip install .If possible, we recommend creating a new conda environment first:
conda create -n sbisandbox python=3.10 && conda activate sbisandboxThis project uses the sbi package and was inspired by the sbibm benchmark suite.
[1]: Cranmer, Kyle et al. “The frontier of simulation-based inference.” Proceedings of the National Academy of Sciences 117 (2019): 30055 - 30062.
[2]: Tejero-Cantero et al., (2020). sbi: A toolkit for simulation-based inference. Journal of Open Source Software, 5(52), 2505, https://doi.org/10.21105/joss.02505
[3]: Lueckmann, Jan-Matthis et al. “Benchmarking Simulation-Based Inference.” International Conference on Artificial Intelligence and Statistics (2021).