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Fast ε-free Inference of Simulation Models with Bayesian Conditional Density Estimation
George Papamakarios, Iain Murray, 2016.
What it's used for here
Advances in Neural Information Processing Systems 29 (NIPS 2016); arXiv:1605.06376 (both pages checked). Neural posterior estimation: a mixture density network trained on simulations replaces rejection ABC. Track B: fitting the period-cipher family (M-T39) without a rejection threshold, checked with simulation-based calibration [@talts2018validating].
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Research copy, for editors and curators. The work is the publisher's, so readers are sent to it. We keep this copy so the citation can be checked if the page changes or goes.
proceedings.neurips.cc · fetched 29 Oct 2026
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Cite it
George Papamakarios, & Iain Murray (2016). Fast ε-free Inference of Simulation Models with Bayesian Conditional Density Estimation. https://proceedings.neurips.cc/paper/2016/hash/6aca97005c68f1206823815f66102863-Abstract.html
@inproceedings{papamakarios2016fast,
author = {George Papamakarios} and {Iain Murray},
title = {Fast ε-free Inference of Simulation Models with Bayesian Conditional Density Estimation},
year = {2016},
url = {https://proceedings.neurips.cc/paper/2016/hash/6aca97005c68f1206823815f66102863-Abstract.html}
}TY - GEN TI - Fast ε-free Inference of Simulation Models with Bayesian Conditional Density Estimation AU - George Papamakarios AU - Iain Murray PY - 2016 UR - https://proceedings.neurips.cc/paper/2016/hash/6aca97005c68f1206823815f66102863-Abstract.html ER -
and . “Fast ε-free Inference of Simulation Models with Bayesian Conditional Density Estimation.” 2016.