Simulation based inference

a.k.a. Approximate Bayesian Computation or Likelihood-Free-Inference

Simulation-based inference, likelihood-free inference, and approximate Bayesian Computation are all terrible descriptions. There are many ways that inference can be based upon simulations, many types of freedom from likelihood and many ways to approximate Bayesian computation. However, all these terms together refer to a particular thing.

TBD: relationship between this and indirect inference. They look similar but tend not to cite each other. Is this a technical or sociological difference?

Miles Cranmer’s Introduction to Simulation-based inference.


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Cranmer, Kyle, Johann Brehmer, and Gilles Louppe. 2020. “The Frontier of Simulation-Based Inference.” Proceedings of the National Academy of Sciences, May.
Cranmer, Kyle, Juan Pavez, and Gilles Louppe. 2015. “Approximating Likelihood Ratios with Calibrated Discriminative Classifiers,” June.
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Durkan, Conor, George Papamakarios, and Iain Murray. 2018. “Sequential Neural Methods for Likelihood-Free Inference,” 9.
Fan, Yanan, David J. Nott, and Scott A. Sisson. 2013. “Approximate Bayesian Computation via Regression Density Estimation.” Stat 2 (1): 34–48.
Forneron, Jean-Jacques, and Serena Ng. 2015. “The ABC of Simulation Estimation with Auxiliary Statistics.” January 6, 2015.
Gourieroux, Christian, and Alain Monfort. 1993. “Simulation-Based Inference: A Survey with Special Reference to Panel Data Models.” Journal of Econometrics 59 (1–2): 5–33.
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Papamakarios, George, and Iain Murray. 2016. “Fast \epsilon -Free Inference of Simulation Models with Bayesian Conditional Density Estimation.” In Advances in Neural Information Processing Systems 29, edited by D. D. Lee, M. Sugiyama, U. V. Luxburg, I. Guyon, and R. Garnett, 1028–36. Curran Associates, Inc.
Rubin, Donald B. 1984. “Bayesianly Justifiable and Relevant Frequency Calculations for the Applied Statistician.” Annals of Statistics 12 (4): 1151–72.
Sisson, S. A., Y. Fan, and Mark M. Tanaka. 2007. “Sequential Monte Carlo Without Likelihoods.” Proceedings of the National Academy of Sciences 104 (6): 1760–65.
Sisson, Scott A., Yanan Fan, and Mark Beaumont. 2018. Handbook of Approximate Bayesian Computation. CRC Press.
Stoye, Markus, Johann Brehmer, Gilles Louppe, Juan Pavez, and Kyle Cranmer. 2018. “Likelihood-Free Inference with an Improved Cross-Entropy Estimator.” August 2, 2018.
Tran, Dustin, Rajesh Ranganath, and David Blei. 2017. “Hierarchical Implicit Models and Likelihood-Free Variational Inference.” In Advances in Neural Information Processing Systems 30, edited by I. Guyon, U. V. Luxburg, S. Bengio, H. Wallach, R. Fergus, S. Vishwanathan, and R. Garnett, 5523–33. Curran Associates, Inc.

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