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A Likelihood-Free Approach to Goal-Oriented Bayesian Optimal
  Experimental Design

A Likelihood-Free Approach to Goal-Oriented Bayesian Optimal Experimental Design

18 August 2024
Atlanta Chakraborty
Xun Huan
Tommie A. Catanach
ArXiv (abs)PDFHTML

Papers citing "A Likelihood-Free Approach to Goal-Oriented Bayesian Optimal Experimental Design"

14 / 14 papers shown
Title
Goal-Oriented Bayesian Optimal Experimental Design for Nonlinear Models using Markov Chain Monte Carlo
Goal-Oriented Bayesian Optimal Experimental Design for Nonlinear Models using Markov Chain Monte Carlo
Shijie Zhong
Wanggang Shen
Tommie A. Catanach
Xun Huan
54
4
0
26 Mar 2024
Bayesian Synthetic Likelihood
Bayesian Synthetic Likelihood
David T. Frazier
Christopher C. Drovandi
David J. Nott
177
220
0
09 May 2023
Modern Bayesian Experimental Design
Modern Bayesian Experimental Design
Tom Rainforth
Adam Foster
Desi R. Ivanova
Freddie Bickford-Smith
86
86
0
28 Feb 2023
Implicit Deep Adaptive Design: Policy-Based Experimental Design without
  Likelihoods
Implicit Deep Adaptive Design: Policy-Based Experimental Design without Likelihoods
Desi R. Ivanova
Adam Foster
Steven Kleinegesse
Michael U. Gutmann
Tom Rainforth
OffRL
117
48
0
03 Nov 2021
Gradient-based Bayesian Experimental Design for Implicit Models using
  Mutual Information Lower Bounds
Gradient-based Bayesian Experimental Design for Implicit Models using Mutual Information Lower Bounds
Steven Kleinegesse
Michael U. Gutmann
FedML
62
25
0
10 May 2021
Bayesian Experimental Design for Implicit Models by Mutual Information
  Neural Estimation
Bayesian Experimental Design for Implicit Models by Mutual Information Neural Estimation
Steven Kleinegesse
Michael U. Gutmann
64
66
0
19 Feb 2020
The frontier of simulation-based inference
The frontier of simulation-based inference
Kyle Cranmer
Johann Brehmer
Gilles Louppe
AI4CE
190
854
0
04 Nov 2019
Efficient MCMC Sampling with Dimension-Free Convergence Rate using
  ADMM-type Splitting
Efficient MCMC Sampling with Dimension-Free Convergence Rate using ADMM-type Splitting
Maxime Vono
Daniel Paulin
Arnaud Doucet
89
37
0
23 May 2019
Efficient Bayesian Experimental Design for Implicit Models
Efficient Bayesian Experimental Design for Implicit Models
Steven Kleinegesse
Michael U. Gutmann
65
50
0
23 Oct 2018
Sequential Neural Likelihood: Fast Likelihood-free Inference with
  Autoregressive Flows
Sequential Neural Likelihood: Fast Likelihood-free Inference with Autoregressive Flows
George Papamakarios
D. Sterratt
Iain Murray
BDL
533
370
0
18 May 2018
Likelihood-free inference by ratio estimation
Likelihood-free inference by ratio estimation
Owen Thomas
Ritabrata Dutta
J. Corander
Samuel Kaski
Michael U. Gutmann
190
151
0
30 Nov 2016
Change-Point Detection in Time-Series Data by Relative Density-Ratio
  Estimation
Change-Point Detection in Time-Series Data by Relative Density-Ratio Estimation
Song Liu
M. Yamada
Nigel Collier
Masashi Sugiyama
66
446
0
02 Mar 2012
Simulation-based optimal Bayesian experimental design for nonlinear
  systems
Simulation-based optimal Bayesian experimental design for nonlinear systems
Xun Huan
Youssef M. Marzouk
86
430
0
20 Aug 2011
Non-linear regression models for Approximate Bayesian Computation
Non-linear regression models for Approximate Bayesian Computation
M. Blum
O. François
225
484
0
24 Sep 2008
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