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2018

Likelihood-free inference with an improved cross-entropy estimator

Stoye, Markus, Brehmer, Johann, Louppe, Gilles et al.

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We extend recent work (Brehmer, et.

al., 2018) that use neural networks as surrogate models for likelihood-free inference.
As in the previous work, we exploit the fact that the joint likelihood ratio and joint score, conditioned on both observed and latent variables, can often be extracted from an implicit generative model or simulator to augment the training data for these surrogate models.
We show how this augmented training data can be used to provide a new cross-entropy estimator, which provides improved sample efficiency compared to previous loss functions exploiting this augmented training data.

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D. B. Rubin: ‘Bayesianly justifiable and relevant frequency calculations for the applied statistician’. Ann. Statist. 12 (4), p. 1151, 1984. URL https://doi.org/10.1214/aos/1176346785
1984
Earlier work this paper cites.
M. A. Beaumont, W. Zhang, and D. J. Balding: ‘Approximate bayesian computation in population genetics’. Genetics 162 (4), p. 2025, 2002
2002
Earlier work this paper cites.
T. Kanamori, S. Hido, and M. Sugiyama: ‘A least-squares approach to direct importance estimation’. Journal of Machine Learning Research 10 (Jul), p. 1391, 2009
2009
Earlier work this paper cites.
Y. Fan, D. J. Nott, and S. A. Sisson: ‘Approximate Bayesian Computation via Regression Density Estimation’. ArXiv e-prints , 2012. arXiv:1212.1479
Original
2012
Earlier work this paper cites.
L. Dinh, D. Krueger, and Y. Bengio: ‘NICE: Non-linear Independent Components Estimation’. ArXiv e-prints , 2014. arXiv:1410.8516
Original
2014
Earlier work this paper cites.
D. Jimenez Rezende and S. Mohamed: ‘Variational Inference with Normalizing Flows’. ArXiv e-prints , 2015. arXiv:1505.05770
Original
2015
Earlier work this paper cites.
K. Cranmer, J. Pavez, and G. Louppe: ‘Approximating Likelihood Ratios with Calibrated Discriminative Classifiers’ , 2015. arXiv:1506.02169
Original
2015
Earlier work this paper cites.
K. Cranmer and G. Louppe: ‘Unifying generative models and exact likelihood-free inference with conditional bijections’. J. Brief Ideas , 2016
2016
Earlier work this paper cites.
L. Dinh, J. Sohl-Dickstein, and S. Bengio: ‘Density estimation using Real NVP’. ArXiv e-prints , 2016. arXiv:1605.08803
Original
2016
Earlier work this paper cites.

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