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Inverse binomial sampling for efficient log-likelihood estimation of simulator models
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README.md

README.md

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. [1]

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.

Code

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).

The code used to produce results in the paper [1] is available in the development repository here.

References

  1. 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.

  1. 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 luigi.acerbi@gmail.com or basvanopheusden@nyu.edu (putting 'IBS' in the subject of the email).

License

The IBS code is released under the terms of the MIT License.

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