GPS-ABC: Gaussian Process Surrogate Approximate Bayesian Computation

13 January 2014

Papers citing "GPS-ABC: Gaussian Process Surrogate Approximate Bayesian Computation"

50 / 67 papers shown

Title
Multilevel neural simulation-based inference Yuga Hikida Ayush Bharti Niall Jeffrey F. Briol 60 0 0 06 Jun 2025
A survey of Monte Carlo methods for noisy and costly densities with application to reinforcement learning and ABC F. Llorente Luca Martino Jesse Read D. Delgado OffRL 165 14 0 03 Jan 2025
Active Sequential Posterior Estimation for Sample-Efficient Simulation-Based Inference Sam Griesemer Defu Cao Zijun Cui Carolina Osorio Yang Liu 125 3 0 07 Dec 2024
Cost-aware Simulation-based Inference Ayush Bharti Daolang Huang Samuel Kaski F. Briol 87 2 0 10 Oct 2024
Bayesian Synthetic Likelihood David T. Frazier Christopher C. Drovandi David J. Nott 191 220 0 09 May 2023
Sequential Neural Score Estimation: Likelihood-Free Inference with Conditional Score Based Diffusion Models Louis Sharrock J. Simons Song Liu Mark Beaumont DiffM 126 39 0 10 Oct 2022
GATSBI: Generative Adversarial Training for Simulation-Based Inference Poornima Ramesh Jan-Matthis Lueckmann Jan Boelts Álvaro Tejero-Cantero David S. Greenberg P. J. Gonçalves Jakob H. Macke GAN 81 37 0 12 Mar 2022
Approximate Bayesian Computation for Physical Inverse Modeling Neel Chatterjee Somya Sharma S. Swisher Snigdhansu Chatterjee 18 0 0 26 Nov 2021
Likelihood-Free Frequentist Inference: Bridging Classical Statistics and Machine Learning for Reliable Simulator-Based Inference Niccolò Dalmasso Luca Masserano David Y. Zhao Rafael Izbicki Ann B. Lee 190 7 0 08 Jul 2021
Deep Bayesian Active Learning for Accelerating Stochastic Simulation D. Wu Ruijia Niu Matteo Chinazzi Alessandro Vespignani Yi-An Ma Rose Yu AI4CE 117 9 0 05 Jun 2021
Warped Gradient-Enhanced Gaussian Process Surrogate Models for Exponential Family Likelihoods with Intractable Normalizing Constants Quan Vu M. Moores A. Zammit‐Mangion 61 1 0 10 May 2021
Approximate Bayesian inference from noisy likelihoods with Gaussian process emulated MCMC Marko Jarvenpaa J. Corander 61 5 0 08 Apr 2021
No-Regret Algorithms for Private Gaussian Process Bandit Optimization Abhimanyu Dubey 66 13 0 24 Feb 2021
Inverse Gaussian Process regression for likelihood-free inference Hongqiao Wang Ziqiao Ao Tengchao Yu Jinglai Li 29 1 0 21 Feb 2021
Benchmarking Simulation-Based Inference Jan-Matthis Lueckmann Jan Boelts David S. Greenberg P. J. Gonçalves Jakob H. Macke 267 198 0 12 Jan 2021
Score Matched Neural Exponential Families for Likelihood-Free Inference Lorenzo Pacchiardi Ritabrata Dutta 234 28 0 20 Dec 2020
On the accept-reject mechanism for Metropolis-Hastings algorithms N. Glatt-Holtz J. Krometis Cecilia F. Mondaini 88 10 0 09 Nov 2020
Likelihood-Free Inference with Deep Gaussian Processes Alexander Aushev Henri Pesonen Markus Heinonen J. Corander Samuel Kaski GP 101 10 0 18 Jun 2020
A Survey of Bayesian Statistical Approaches for Big Data Farzana Jahan Insha Ullah Kerrie Mengersen 102 14 0 08 Jun 2020
Bayesian Computation with Intractable Likelihoods M. Moores A. Pettitt Kerrie Mengersen TPM 102 3 0 08 Apr 2020
Differentiable Likelihoods for Fast Inversion of 'Likelihood-Free' Dynamical Systems Hans Kersting N. Krämer Martin Schiegg Christian Daniel Michael Tiemann Philipp Hennig 70 21 0 21 Feb 2020
On Contrastive Learning for Likelihood-free Inference Conor Durkan Iain Murray George Papamakarios BDL 233 124 0 10 Feb 2020
The frontier of simulation-based inference Kyle Cranmer Johann Brehmer Gilles Louppe AI4CE 266 857 0 04 Nov 2019
Neural Density Estimation and Likelihood-free Inference George Papamakarios BDL DRL 100 47 0 29 Oct 2019
Batch simulations and uncertainty quantification in Gaussian process surrogate approximate Bayesian computation Marko Jarvenpaa Aki Vehtari Pekka Marttinen 64 15 0 14 Oct 2019
Distance-learning For Approximate Bayesian Computation To Model a Volcanic Eruption Lorenzo Pacchiardi Pierre Künzli Marcel Schoengens B. Chopard Ritabrata Dutta 65 13 0 28 Sep 2019
A review of Approximate Bayesian Computation methods via density estimation: inference for simulator-models C. Grazian Yanan Fan TPM 51 22 0 06 Sep 2019
Bayesian Deconditional Kernel Mean Embeddings Kelvin Hsu F. Ramos CML BDL 46 9 0 01 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 73 8 0 27 May 2019
Finding our Way in the Dark: Approximate MCMC for Approximate Bayesian Methods Evgeny Levi Radu V. Craiu 68 6 0 16 May 2019
Parallel Gaussian process surrogate Bayesian inference with noisy likelihood evaluations Marko Jarvenpaa Michael U. Gutmann Aki Vehtari Pekka Marttinen 118 41 0 03 May 2019
Likelihood-free MCMC with Amortized Approximate Ratio Estimators Joeri Hermans Volodimir Begy Gilles Louppe 124 20 0 10 Mar 2019
Bayesian Learning of Conditional Kernel Mean Embeddings for Automatic Likelihood-Free Inference Kelvin Hsu F. Ramos 59 12 0 03 Mar 2019
Bayesian inference using synthetic likelihood: asymptotics and adjustments David T. Frazier David J. Nott Christopher C. Drovandi Robert Kohn 88 41 0 13 Feb 2019
Credit Assignment Techniques in Stochastic Computation Graphs T. Weber N. Heess Lars Buesing David Silver 98 45 0 07 Jan 2019
Information geometry for approximate Bayesian computation K. Spiliopoulos 36 1 0 05 Dec 2018
Machine Learning Accelerated Likelihood-Free Event Reconstruction in Dark Matter Direct Detection U. Simola B. Pelssers D. Barge J. Conrad J. Corander 134 11 0 23 Oct 2018
A Function Emulation Approach for Doubly Intractable Distributions Jaewoo Park M. Haran TPM 79 15 0 20 Jun 2018
Accelerating delayed-acceptance Markov chain Monte Carlo algorithms Samuel Wiqvist Umberto Picchini J. Forman Kresten Lindorff-Larsen Wouter Boomsma 40 8 0 15 Jun 2018
Likelihood-free inference with emulator networks Jan-Matthis Lueckmann Giacomo Bassetto Theofanis Karaletsos Jakob H. Macke 181 128 0 23 May 2018
Sequential Neural Likelihood: Fast Likelihood-free Inference with Autoregressive Flows George Papamakarios D. Sterratt Iain Murray BDL 552 370 0 18 May 2018
Approximating the Likelihood in Approximate Bayesian Computation Christopher C. Drovandi Clara Grazian Kerrie Mengersen Christian P. Robert 80 12 0 18 Mar 2018
Kernel Recursive ABC: Point Estimation with Intractable Likelihood T. Kajihara Motonobu Kanagawa Keisuke Yamazaki Kenji Fukumizu 96 13 0 23 Feb 2018
Variational Inference over Non-differentiable Cardiac Simulators using Bayesian Optimization Adam McCarthy Blanca Rodriguez A. Mincholé 155 5 0 09 Dec 2017
Bootstrapped synthetic likelihood R. Everitt 231 14 0 15 Nov 2017
Flexible statistical inference for mechanistic models of neural dynamics Jan-Matthis Lueckmann P. J. Gonçalves Giacomo Bassetto Kaan Öcal M. Nonnenmacher Jakob H. Macke 193 249 0 06 Nov 2017
Marginal sequential Monte Carlo for doubly intractable models R. Everitt D. Prangle Philip Maybank M. Bell 53 8 0 12 Oct 2017
Efficient acquisition rules for model-based approximate Bayesian computation Marko Jarvenpaa Michael U. Gutmann Arijus Pleska Aki Vehtari Pekka Marttinen TPM 202 69 0 03 Apr 2017
Likelihood-free inference by ratio estimation Owen Thomas Ritabrata Dutta J. Corander Samuel Kaski Michael U. Gutmann 234 151 0 30 Nov 2016
Gaussian process modeling in approximate Bayesian computation to estimate horizontal gene transfer in bacteria Marko Jarvenpaa Michael U. Gutmann Aki Vehtari Pekka Marttinen 206 41 0 20 Oct 2016