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We introduce the sequential neural posterior and likelihood approximation (SNPLA) algorithm.
Normalizing flows for probabilistic modeling and inference
G. Papamakarios, E. Nalisnick, D. J. Rezende, S. Mohamed, and B. Lakshminarayanan · 1912
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A quantitative description of membrane current and its application to conduction and excitation in nerve
A. L. Hodgkin and A. F. Huxley · 1952
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Approximate bayesian computation in population genetics
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Adaptive approximate bayesian computation
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Statistical inference for noisy nonlinear ecological dynamic systems
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Approximate bayesian computational methods
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Variational inference with normalizing flows
D. Rezende and S. Mohamed · 2015
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Bayesian optimization for likelihood-free inference of simulator-based statistical models
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Fast ε \varepsilon -free inference of simulation models with bayesian conditional density estimation
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Masked autoregressive flow for density estimation
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Deep sets
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Pytorch: An imperative style, high-performance deep learning library
A. Paszke, S. Gross, F. Massa, A. Lerer, J. Bradbury, G. Chanan, T. Killeen, Z. Lin, N. Gimelshein, L. Antiga, A. Desmaison, A. Kopf, E. Yang, Z. DeVito, M. Raison, A. Tejani, S. Chilamkurthy, B. Steiner, L. Fang, J. Bai, and S. Chintala · 2019
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Partially exchangeable networks and architectures for learning summary statistics in approximate Bayesian computation
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Smart computational exploration of stochastic gene regulatory network models using human-in-the-loop semi-supervised learning
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The frontier of simulation-based inference
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Likelihood-free MCMC with amortized approximate ratio estimators
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Sequential neural methods for likelihood-free inference
C. Durkan, G. Papamakarios, and I. Murray · 2018
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Bayesian synthetic likelihood
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Validating bayesian inference algorithms with simulation-based calibration
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Neural spline flows
C. Durkan, A. Bekasov, I. Murray, and G. Papamakarios · 2019
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Automatic posterior transformation for likelihood-free inference
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nflows: normalizing flows in PyTorch, Nov. 2020a
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Normalizing flows: An introduction and review of current methods
I. Kobyzev, S. Prince, and M. Brubaker · 2020
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Bayesflow: Learning complex stochastic models with invertible neural networks
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sbi: A toolkit for simulation-based inference
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Likelihood-free inference by ratio estimation
O. Thomas, R. Dutta, J. Corander, S. Kaski, and M. U. Gutmann · 2020
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Benchmarking simulation-based inference
J.-M. Lueckmann, J. Boelts, D. S. Greenberg, P. J. Gonçalves, and J. H. Macke · 2021
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