Credits

Algorithms

delfi implements algorithms from the following papers:

  • Fast ε-free Inference of Simulation Models with Bayesian Conditional Density Estimation
    by Papamakarios & Murray (NeurIPS 2016)
    [PDF] [BibTeX]

  • Flexible statistical inference for mechanistic models of neural dynamics
    by Lueckmann, Goncalves, Bassetto, Öcal, Nonnenmacher & Macke (NeurIPS 2017)
    [PDF] [BibTeX]

  • Automatic posterior transformation for likelihood-free inference
    by Greenberg, Nonnenmacher & Macke (ICML 2019)
    [PDF] [BibTeX]

We refer to these algorithms as SNPE-A, SNPE-B, and SNPE-C/APT, respectively.

Code

delfi.distribution and delfi.neuralnet build on code written by George Papamakarios. The separation of classes in delfi is partially inspired by the design of ELFI: Engine for Likelihood-Free Inference.

Modified work Copyright (c) 2019, David Greenberg, Marcel Nonnemacher, Jan-Matthis Lueckmann, Pedro J. Goncalves, Kaan Öcal, Jakob H. Macke
Modified work Copyright (c) 2017, Jan-Matthis Lueckmann, Pedro J. Goncalves, Jakob H. Macke
Original work Copyright (c) 2016, George Papamakarios

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