Inverse binomial sampling (IBS)
What is it
IBS is a technique to obtain unbiased, efficient estimates of the log-likelihood of a model by simulation. 
The typical scenario is the case in which you have an implicit model from which you can randomly draw synthetic observations (for a given parameter vector), but cannot evaluate the log-likelihood analytically or numerically. In other words, IBS affords likelihood-based inference for likelihood-free models.
This repository stores basic and advanced implementations and example usages of IBS in various programming languages for users of the method. For the moment, we only have a MATLAB implementation, but we plan to include other ones (e.g., Python).
- van Opheusden*, B., Acerbi*, L. & Ma, W.J. (2020). Unbiased and efficient log-likelihood estimation with inverse binomial sampling. arXiv preprint. (* equal contribution) (preprint on arXiv)
You can cite IBS in your work with something along the lines of
We estimated the log-likelihood using inverse binomial sampling (IBS; van Opheusden et al., 2019), a technique that produces unbiased and efficient estimates of the log-likelihood via simulation.
If you use IBS in conjunction with Bayesian Adaptive Direct Search, as recommended in the paper, you could add
We obtained maximum-likelihood estimates of the model parameters via Bayesian Adaptive Direct Search (BADS; Acerbi & Ma, 2017), a hybrid Bayesian optimization algorithm which affords stochastic objective evaluations.
- Acerbi, L. & Ma, W. J. (2017). Practical Bayesian optimization for model fitting with Bayesian Adaptive Direct Search. In Advances in Neural Information Processing Systems 30:1834-1844.
Besides formal citations, you can demonstrate your appreciation for our work in the following ways:
- Star the IBS repository on GitHub;
- Follow us on Twitter (Luigi, Bas) for updates about IBS and other projects we are involved with;
- Tell us about your model-fitting problem and your experience with IBS (positive or negative) at firstname.lastname@example.org or email@example.com (putting 'IBS' in the subject of the email).
The IBS code is released under the terms of the MIT License.