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How can one perform Bayesian inference on stochastic simulators with intractable likelihoods? A recent approach is to learn the posterior from adaptively proposed simulations using neural network-based conditional density estimators.
Analytical note on certain rhythmic relations in organic systems
Lotka, A. J · 1920
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Nonlinear aspects of competition between three species
May, R. M. and Leonard, W. J · 1975
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Pritchard, J. K., Seielstad, M. T., Perez-Lezaun, A., and Feldman, M. W · 1999
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Approximate bayesian computation in population genetics
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Markov chain monte carlo without likelihoods
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Mobility promotes and jeopardizes biodiversity in rock–paper–scissors games
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Sequential monte carlo without likelihoods
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Particle markov chain monte carlo methods
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Non-linear regression models for approximate bayesian computation
Blum, M. G. B. and François, O · 2010
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Statistical inference for noisy nonlinear ecological dynamic systems
Wood, S. N · 2010
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Constructing summary statistics for approximate bayesian computation: semi-automatic approximate bayesian computation
Fearnhead, P. and Prangle, D · 2012
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A kernel two-sample test
Gretton, A., Borgwardt, K. M., Rasch, M. J., Schölkopf, B., and Smola, A · 2012
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A comparative review of dimension reduction methods in approximate bayesian computation
Blum, M. G., Nunes, M. A., Prangle, D., Sisson, S. A., et al · 2013
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Approximate bayesian computation via regression density estimation
Fan, Y., Nott, D. J., and Sisson, S. A · 2013
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Learning phrase representations using rnn encoder-decoder for statistical machine translation
Cho, K., Van Merriënboer, B., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H., and Bengio, Y · 2014
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A generalized, likelihood-free method for posterior estimation
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Approximating likelihood ratios with calibrated discriminative classifiers
Cranmer, K., Pavez, J., and Louppe, G · 2015
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Likelihood free inference for markov processes: a comparison
Owen, J., Wilkinson, D. J., and Gillespie, C. S · 2015
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Variational inference with normalizing flows
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Masked autoregressive flow for density estimation
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Vision-as-inverse-graphics: Obtaining a rich 3d explanation of a scene from a single image
Romaszko, L., Williams, C. K., Moreno, P., and Kohli, P · 2017
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Zaheer, M., Kottur, S., Ravanbakhsh, S., Poczos, B., Salakhutdinov, R. R., and Smola, A. J · 2017
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Mining gold from implicit models to improve likelihood-free inference
Brehmer, J., Louppe, G., Pavez, J., and Cranmer, K · 2018
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A likelihood-free inference framework for population genetic data using exchangeable neural networks
Chan, J., Perrone, V., Spence, J. P., Jenkins, P. A., Mathieson, S., and Song, Y. S · 2018
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Bayesian optimization for likelihood-free inference of simulator-based statistical models
Gutmann, M. U. and Corander, J · 2016
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Learning in implicit generative models
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Using synthetic data to train neural networks is model-based reasoning
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Handbook of Approximate Bayesian Computation
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Probabilistic symmetry and invariant neural networks
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