Likelihood-free inference with an improved cross-entropy estimator

By Markus Stoye, J. Brehmer, Gilles Louppe, Juan Pavez, Kyle Cranmer. 2018

53 citations recorded by the source corpus.

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.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition, Statistics and Probability, Probabilistic & Bayesian Methods

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Citations in the corpus

The frontier of simulation-based inference (2019, background): “The resulting supervised learning task can often be solved more efficiently, ultimately improving the sample efficiency in the inference task (9, 44, 45, 66).”
Sequential Neural Likelihood: Fast Likelihood-free Inference with Autoregressive Flows (2018, background): “Rather than learning the likelihood p ( x | θ ), an alternative approach is learning the likelihood ratio p ( x | θ 1 ) /p ( x | θ 2 ) [11, 12, 15, 30, 61, 74], or the likelihood-to-marginal ratio p ( x | θ ) /p ( x ) [19, 33, 77].”
Constraining Effective Field Theories with Machine Learning. (2018, methodology): “A recent set of publications outlines a new approach to likelihood-free inference [9–12].”
Mining for Dark Matter Substructure: Inferring Subhalo Population Properties from Strong Lenses with Machine Learning (2019, methodology): “As shown in Stoye et al. (2018), this “ALICES” loss functional is minimized by the function ( ) [ ( )] ( ∣ ) ( )J J J º = + g x L g x r x , arg min , 1 1 , 23 g * one-to-one with the likelihood ratio function ( ∣ ) ( ∣ ) ( ) ( ) ( ) ( )J J J J º = - r x p x p x g x g x 1 , , .”