Dynamic Likelihood-free Inference via Ratio Estimation (DIRE)

23 October 2018

Papers citing "Dynamic Likelihood-free Inference via Ratio Estimation (DIRE)"

8 / 8 papers shown

Title
Statistical applications of contrastive learning Michael U. Gutmann Steven Kleinegesse Benjamin Rhodes 24 7 0 29 Apr 2022
Amortised Likelihood-free Inference for Expensive Time-series Simulators with Signatured Ratio Estimation Joel Dyer Patrick W Cannon Sebastian M. Schmon 28 9 0 23 Feb 2022
Benchmarking Simulation-Based Inference Jan-Matthis Lueckmann Jan Boelts David S. Greenberg P. J. Gonçalves Jakob H. Macke 104 185 0 12 Jan 2021
Neural Approximate Sufficient Statistics for Implicit Models Yanzhi Chen Dinghuai Zhang Michael U. Gutmann Aaron Courville Zhanxing Zhu 30 79 0 20 Oct 2020
Marginally-calibrated deep distributional regression Nadja Klein David J. Nott M. Smith UQCV 32 14 0 26 Aug 2019
MadMiner: Machine learning-based inference for particle physics Johann Brehmer F. Kling Irina Espejo Kyle Cranmer 21 113 0 24 Jul 2019
Mining gold from implicit models to improve likelihood-free inference Johann Brehmer Gilles Louppe J. Pavez Kyle Cranmer AI4CE TPM 38 180 0 30 May 2018
Likelihood-free inference by ratio estimation Owen Thomas Ritabrata Dutta J. Corander Samuel Kaski Michael U. Gutmann 42 145 0 30 Nov 2016