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On contrastive learning for likelihood-free inference, 2020
Original
Durkan, C., Murray, I., and Papamakarios, G · 2002
Earlier work this paper cites.
Non-linear regression models for approximate bayesian computation
Blum, M. G. B. and François, O · 2009
Earlier work this paper cites.
Flowpm: Distributed tensorflow implementation of the fastpm cosmological n-body solver, 2020
Original
Modi, C., Lanusse, F., and Seljak, U · 2010
Earlier work this paper cites.
Statistical inference for noisy nonlinear ecological dynamic systems
Wood, S. N · 2010
Earlier work this paper cites.
Nice: Non-linear independent components estimation, 2014
Original
Dinh, L., Krueger, D., and Bengio, Y · 2014
Earlier work this paper cites.
High-dimensional density ratio estimation with extensions to approximate likelihood computation
Original
Izbicki, R., Lee, A. B., and Schafer, C. M · 2014
Earlier work this paper cites.
Approximating likelihood ratios with calibrated discriminative classifiers, 2015
Original
Cranmer, K., Pavez, J., and Louppe, G · 2015
Earlier work this paper cites.

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Variational inference with normalizing flows, 2015
Original
Rezende, D. J. and Mohamed, S · 2015
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Density estimation using real nvp, 2016
Original
Dinh, L., Sohl-Dickstein, J., and Bengio, S · 2016
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Likelihood-free inference by ratio estimation, 2016
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Thomas, O., Dutta, R., Corander, J., Kaski, S., and Gutmann, M. U · 2016
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Sigmoid-weighted linear units for neural network function approximation in reinforcement learning, 2017
Elfwing, S., Uchibe, E., and Doya, K · 2017
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Flexible statistical inference for mechanistic models of neural dynamics, 2017
Lueckmann, J.-M., Goncalves, P. J., Bassetto, G., Öcal, K., Nonnenmacher, M., and Macke, J. H · 2017

Then

Fast ϵ \epsilon -free inference of simulation models with bayesian conditional density estimation, 2018
Papamakarios, G. and Murray, I · 2018
Later among the works it cites.
Sequential neural likelihood: Fast likelihood-free inference with autoregressive flows, 2018
Original
Papamakarios, G., Sterratt, D. C., and Murray, I · 2018
Later among the works it cites.
Automatic posterior transformation for likelihood-free inference, 2019
Greenberg, D. S., Nonnenmacher, M., and Macke, J. H · 2019
Later among the works it cites.
Mining gold from implicit models to improve likelihood-free inference
Brehmer, J., Louppe, G., Pavez, J., and Cranmer, K · 2020
Later among the works it cites.
Efficient and modular implicit differentiation, 2021
Original
Blondel, M., Berthet, Q., Cuturi, M., Frostig, R., Hoyer, S., Llinares-López, F., Pedregosa, F., and Vert, J.-P · 2021
Later among the works it cites.

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2022

Neural Posterior Estimation with Differentiable Simulators

Zeghal, Justine, Lanusse, François, Boucaud, Alexandre et al.

Understand

Simulation-Based Inference (SBI) is a promising Bayesian inference framework that alleviates the need for analytic likelihoods to estimate posterior distributions.

Recent advances using neural density estimators in SBI algorithms have demonstrated the ability to achieve high-fidelity posteriors, at the expense of a large number of simulations ; which makes their application potentially very time-consuming when using complex physical simulations.
In this work we focus on boosting the sample-efficiency of posterior density estimation using the gradients of the simulator.
We present a new method to perform Neural Posterior Estimation (NPE) with a differentiable simulator.

Built on

On contrastive learning for likelihood-free inference, 2020
Original
Durkan, C., Murray, I., and Papamakarios, G · 2002
Earlier work this paper cites.
Non-linear regression models for approximate bayesian computation
Blum, M. G. B. and François, O · 2009
Earlier work this paper cites.
Flowpm: Distributed tensorflow implementation of the fastpm cosmological n-body solver, 2020
Original
Modi, C., Lanusse, F., and Seljak, U · 2010
Earlier work this paper cites.
Statistical inference for noisy nonlinear ecological dynamic systems
Wood, S. N · 2010
Earlier work this paper cites.
Nice: Non-linear independent components estimation, 2014
Original
Dinh, L., Krueger, D., and Bengio, Y · 2014
Earlier work this paper cites.
High-dimensional density ratio estimation with extensions to approximate likelihood computation
Original
Izbicki, R., Lee, A. B., and Schafer, C. M · 2014
Earlier work this paper cites.
Approximating likelihood ratios with calibrated discriminative classifiers, 2015
Original
Cranmer, K., Pavez, J., and Louppe, G · 2015
Earlier work this paper cites.

Similar

Variational inference with normalizing flows, 2015
Original
Rezende, D. J. and Mohamed, S · 2015
Cited alongside, same era.
Density estimation using real nvp, 2016
Original
Dinh, L., Sohl-Dickstein, J., and Bengio, S · 2016
Cited alongside, same era.
Likelihood-free inference by ratio estimation, 2016
Original
Thomas, O., Dutta, R., Corander, J., Kaski, S., and Gutmann, M. U · 2016
Cited alongside, same era.
Sigmoid-weighted linear units for neural network function approximation in reinforcement learning, 2017
Elfwing, S., Uchibe, E., and Doya, K · 2017
Cited alongside, same era.
Flexible statistical inference for mechanistic models of neural dynamics, 2017
Lueckmann, J.-M., Goncalves, P. J., Bassetto, G., Öcal, K., Nonnenmacher, M., and Macke, J. H · 2017

Then

Fast ϵ \epsilon -free inference of simulation models with bayesian conditional density estimation, 2018
Papamakarios, G. and Murray, I · 2018
Later among the works it cites.
Sequential neural likelihood: Fast likelihood-free inference with autoregressive flows, 2018
Original
Papamakarios, G., Sterratt, D. C., and Murray, I · 2018
Later among the works it cites.
Automatic posterior transformation for likelihood-free inference, 2019
Greenberg, D. S., Nonnenmacher, M., and Macke, J. H · 2019
Later among the works it cites.
Mining gold from implicit models to improve likelihood-free inference
Brehmer, J., Louppe, G., Pavez, J., and Cranmer, K · 2020
Later among the works it cites.
Efficient and modular implicit differentiation, 2021
Original
Blondel, M., Berthet, Q., Cuturi, M., Frostig, R., Hoyer, S., Llinares-López, F., Pedregosa, F., and Vert, J.-P · 2021
Later among the works it cites.