Sequential Neural Likelihood: Fast Likelihood-free Inference with Autoregressive Flows

18 May 2018

Papers citing "Sequential Neural Likelihood: Fast Likelihood-free Inference with Autoregressive Flows"

50 / 202 papers shown

Title
Neural Approximate Sufficient Statistics for Implicit Models Yanzhi Chen Dinghuai Zhang Michael U. Gutmann Aaron Courville Zhanxing Zhu 11 79 0 20 Oct 2020
ABC-Di: Approximate Bayesian Computation for Discrete Data I. Auzina Jakub M. Tomczak 4 0 0 19 Oct 2020
Sequential Likelihood-Free Inference with Neural Proposal Dongjun Kim Kyungwoo Song Yoon-Yeong Kim Yongjin Shin Wanmo Kang Il-Chul Moon Weonyoung Joo 11 2 0 15 Oct 2020
Error-guided likelihood-free MCMC Volodimir Begy Erich Schikuta 11 3 0 13 Oct 2020
Simulation-based inference methods for particle physics Johann Brehmer Kyle Cranmer AI4CE 8 21 0 13 Oct 2020
OutbreakFlow: Model-based Bayesian inference of disease outbreak dynamics with invertible neural networks and its application to the COVID-19 pandemics in Germany Stefan T. Radev Frederik Graw Simiao Chen N. Mutters V. Eichel T. Bärnighausen Ullrich Kothe 16 29 0 01 Oct 2020
Unifying supervised learning and VAEs -- coverage, systematics and goodness-of-fit in normalizing-flow based neural network models for astro-particle reconstructions T. Glüsenkamp 6 1 0 13 Aug 2020
Variational Inference with Continuously-Indexed Normalizing Flows Anthony L. Caterini R. Cornish Dino Sejdinovic Arnaud Doucet BDL 13 19 0 10 Jul 2020
Transformations in Semi-Parametric Bayesian Synthetic Likelihood Jacob W. Priddle Christopher C. Drovandi 6 2 0 03 Jul 2020
Likelihood-Free Inference with Deep Gaussian Processes Alexander Aushev Henri Pesonen Markus Heinonen J. Corander Samuel Kaski GP 11 10 0 18 Jun 2020
Graphical Normalizing Flows Antoine Wehenkel Gilles Louppe TPM BDL 8 36 0 03 Jun 2020
Fully probabilistic quasar continua predictions near Lyman-α with conditional neural spline flows D. Reiman John Tamanas J. Prochaska Dominika Ďurovčíková 10 6 0 31 May 2020
Amortized Bayesian model comparison with evidential deep learning Stefan T. Radev Marco D’Alessandro U. Mertens A. Voss Ullrich Kothe Paul-Christian Burkner BDL 18 32 0 22 Apr 2020
Flows for simultaneous manifold learning and density estimation Johann Brehmer Kyle Cranmer DRL AI4CE 15 157 0 31 Mar 2020
Coping With Simulators That Don't Always Return Andrew Warrington Saeid Naderiparizi Frank D. Wood 6 4 0 28 Mar 2020
Sequential Bayesian Experimental Design for Implicit Models via Mutual Information Steven Kleinegesse Christopher C. Drovandi Michael U. Gutmann 6 28 0 20 Mar 2020
BayesFlow: Learning complex stochastic models with invertible neural networks Stefan T. Radev U. Mertens A. Voss Lynton Ardizzone Ullrich Kothe BDL 9 182 0 13 Mar 2020
Confidence Sets and Hypothesis Testing in a Likelihood-Free Inference Setting Niccolò Dalmasso Rafael Izbicki Ann B. Lee 9 20 0 24 Feb 2020
Bayesian Experimental Design for Implicit Models by Mutual Information Neural Estimation Steven Kleinegesse Michael U. Gutmann 14 64 0 19 Feb 2020
Black-Box Optimization with Local Generative Surrogates S. Shirobokov V. Belavin Michael Kagan Andrey Ustyuzhanin A. G. Baydin 6 3 0 11 Feb 2020
On Contrastive Learning for Likelihood-free Inference Conor Durkan Iain Murray George Papamakarios BDL 34 117 0 10 Feb 2020
Probabilistic Software Modeling: A Data-driven Paradigm for Software Analysis Hannes Thaller L. Linsbauer Rudolf Ramler Alexander Egyed 16 3 0 17 Dec 2019
Normalizing Flows for Probabilistic Modeling and Inference George Papamakarios Eric T. Nalisnick Danilo Jimenez Rezende S. Mohamed Balaji Lakshminarayanan TPM AI4CE 11 1,618 0 05 Dec 2019
The frontier of simulation-based inference Kyle Cranmer Johann Brehmer Gilles Louppe AI4CE 13 820 0 04 Nov 2019
Neural Density Estimation and Likelihood-free Inference George Papamakarios BDL DRL 8 44 0 29 Oct 2019
Batch simulations and uncertainty quantification in Gaussian process surrogate approximate Bayesian computation Marko Jarvenpaa Aki Vehtari Pekka Marttinen 15 15 0 14 Oct 2019
Distilling Importance Sampling for Likelihood Free Inference D. Prangle Cecilia Viscardi 11 3 0 08 Oct 2019
Inference of a mesoscopic population model from population spike trains M. Slawski A. Longtin E. Ben-David 11 12 0 03 Oct 2019
A review of Approximate Bayesian Computation methods via density estimation: inference for simulator-models C. Grazian Yanan Fan TPM 11 22 0 06 Sep 2019
Mining for Dark Matter Substructure: Inferring subhalo population properties from strong lenses with machine learning Johann Brehmer S. Mishra-Sharma Joeri Hermans Gilles Louppe Kyle Cranmer 13 72 0 04 Sep 2019
Marginally-calibrated deep distributional regression Nadja Klein David J. Nott M. Smith UQCV 14 14 0 26 Aug 2019
Modeling the Gaia Color-Magnitude Diagram with Bayesian Neural Flows to Constrain Distance Estimates M. Cranmer Richard Galvez L. Anderson D. Spergel S. Ho 14 7 0 21 Aug 2019
Unconstrained Monotonic Neural Networks Antoine Wehenkel Gilles Louppe TPM 18 146 0 14 Aug 2019
MadMiner: Machine learning-based inference for particle physics Johann Brehmer F. Kling Irina Espejo Kyle Cranmer 19 111 0 24 Jul 2019
Black-Box Inference for Non-Linear Latent Force Models W. Ward Tom Ryder D. Prangle Mauricio A. Alvarez DRL 16 14 0 21 Jun 2019
Neural Spline Flows Conor Durkan Artur Bekasov Iain Murray George Papamakarios DRL 19 739 0 10 Jun 2019
Cubic-Spline Flows Conor Durkan Artur Bekasov Iain Murray George Papamakarios TPM 40 57 0 05 Jun 2019
Effective LHC measurements with matrix elements and machine learning Johann Brehmer Kyle Cranmer Irina Espejo F. Kling Gilles Louppe J. Pavez 10 14 0 04 Jun 2019
Validation of Approximate Likelihood and Emulator Models for Computationally Intensive Simulations Niccolò Dalmasso Ann B. Lee Rafael Izbicki T. Pospisil Ilmun Kim Chieh-An Lin 11 8 0 27 May 2019
Real-time Approximate Bayesian Computation for Scene Understanding J. Felip Nilesh A. Ahuja D. Gómez‐Gutiérrez Omesh Tickoo Vikash K. Mansinghka 6 1 0 22 May 2019
Automatic Posterior Transformation for Likelihood-Free Inference David S. Greenberg M. Nonnenmacher Jakob H. Macke 6 316 0 17 May 2019
Autoregressive Energy Machines C. Nash Conor Durkan 12 55 0 11 Apr 2019
Robust Approximate Bayesian Inference with Synthetic Likelihood David T. Frazier Christopher C. Drovandi 16 44 0 09 Apr 2019
Likelihood-free MCMC with Amortized Approximate Ratio Estimators Joeri Hermans Volodimir Begy Gilles Louppe 17 20 0 10 Mar 2019
Sequential Neural Methods for Likelihood-free Inference Conor Durkan George Papamakarios Iain Murray BDL 23 24 0 21 Nov 2018
Likelihood-free inference with an improved cross-entropy estimator M. Stoye Johann Brehmer Gilles Louppe J. Pavez Kyle Cranmer FedML UQCV BDL 20 48 0 02 Aug 2018
Accelerating delayed-acceptance Markov chain Monte Carlo algorithms Samuel Wiqvist Umberto Picchini J. Forman Kresten Lindorff-Larsen Wouter Boomsma 6 8 0 15 Jun 2018
Mining gold from implicit models to improve likelihood-free inference Johann Brehmer Gilles Louppe J. Pavez Kyle Cranmer AI4CE TPM 12 178 0 30 May 2018
Likelihood-free inference with emulator networks Jan-Matthis Lueckmann Giacomo Bassetto Theofanis Karaletsos Jakob H. Macke 6 124 0 23 May 2018
An automatic adaptive method to combine summary statistics in approximate Bayesian computation Jonathan U. Harrison R. Baker 17 17 0 07 Mar 2017