Reference : Mining gold from implicit models to improve likelihood-free inference
E-prints/Working papers : Already available on another site
Physical, chemical, mathematical & earth Sciences : Physics
Engineering, computing & technology : Computer science
http://hdl.handle.net/2268/226050
Mining gold from implicit models to improve likelihood-free inference
English
Brehmer, Johann [> >]
Louppe, Gilles mailto [Université de Liège - ULiège > Dép. d'électric., électron. et informat. (Inst.Montefiore) > Big Data >]
Pavez, Juan [> >]
Cranmer, Kyle [> >]
1-May-2018
No
[en] Statistics - Machine Learning ; Computer Science - Learning ; High Energy Physics - Phenomenology ; Physics - Data Analysis ; Statistics and Probability
[en] Simulators often provide the best description of real-world phenomena; however, they also lead to challenging inverse problems because the density they implicitly define is often intractable. We present a new suite of simulation-based inference techniques that go beyond the traditional Approximate Bayesian Computation approach, which struggles in a high-dimensional setting, and extend methods that use surrogate models based on neural networks. We show that additional information, such as the joint likelihood ratio and the joint score, can often be extracted from simulators and used to augment the training data for these surrogate models. Finally, we demonstrate that these new techniques are more sample efficient and provide higher-fidelity inference than traditional methods.
Researchers
http://hdl.handle.net/2268/226050
https://arxiv.org/abs/1805.12244
Code available at https://github.com/johannbrehmer/simulator-mining-example
https://arxiv.org/abs/1805.12244

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