• Corpus ID: 211171409

Bayesian Experimental Design for Implicit Models by Mutual Information Neural Estimation

@article{Kleinegesse2020BayesianED,
  title={Bayesian Experimental Design for Implicit Models by Mutual Information Neural Estimation},
  author={Steven Kleinegesse and Michael U Gutmann},
  journal={ArXiv},
  year={2020},
  volume={abs/2002.08129}
}
Implicit stochastic models, where the data-generation distribution is intractable but sampling is possible, are ubiquitous in the natural sciences. The models typically have free parameters that need to be inferred from data collected in scientific experiments. A fundamental question is how to design the experiments so that the collected data are most useful. The field of Bayesian experimental design advocates that, ideally, we should choose designs that maximise the mutual information (MI… 
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References

SHOWING 1-10 OF 43 REFERENCES
Monte Carlo Gradient Estimation in Machine Learning
TLDR
A broad and accessible survey of the methods for Monte Carlo gradient estimation in machine learning and across the statistical sciences, exploring three strategies--the pathwise, score function, and measure-valued gradient estimators--exploring their historical developments, derivation, and underlying assumptions.
Sequential Bayesian Experimental Design for Implicit Models via Mutual Information
TLDR
This work devises a novel sequential design framework for parameter estimation that uses the Mutual Information between model parameters and simulated data as a utility function to find optimal experimental designs, which has not been done before for implicit models.
A Unified Stochastic Gradient Approach to Designing Bayesian-Optimal Experiments
We introduce a fully stochastic gradient based approach to Bayesian optimal experimental design (BOED). Our approach utilizes variational lower bounds on the expected information gain (EIG) of an
On Variational Bounds of Mutual Information
TLDR
This work introduces a continuum of lower bounds that encompasses previous bounds and flexibly trades off bias and variance and demonstrates the effectiveness of these new bounds for estimation and representation learning.
Variational Bayesian Optimal Experimental Design
TLDR
This work introduces several classes of fast EIG estimators by building on ideas from amortized variational inference, and shows theoretically and empirically that these estimators can provide significant gains in speed and accuracy over previous approaches.
Adaptive Gaussian Copula ABC
TLDR
This work presents a simple yet effective ABC algorithm based on the combination of two classical ABC approaches --- regression ABC and sequential ABC that first target another auxiliary distribution that can be learned accurately by existing methods, through which the desired posterior is learned with the help of a Gaussian copula.
Likelihood-Free Extensions for Bayesian Sequentially Designed Experiments
TLDR
In this work, likelihood-free extensions of the standard SMC algorithm are proposed and a specific simulation-based approximation of the likelihood known as the synthetic likelihood is investigated.
...
...