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Likelihood-free inference refers to inference when a likelihood function cannot be explicitly evaluated, which is often the case for models based on simulators.
Exact stochastic simulation of coupled chemical reactions
Gillespie, D. T. (1977) · 1977
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Keeping the neural networks simple by minimizing the description length of the weights
Hinton, G. E. and Van Camp, D. (1993) · 1993
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Mixture density networks
Bishop, C. M. (1994) · 1994
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Algorithm 778: L-BFGS-B: Fortran subroutines for large-scale bound-constrained optimization
Zhu, C., Byrd, R. H., Lu, P., and Nocedal, J. (1997) · 1997
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Stochastic Modelling for Systems Biology
Wilkinson, D. J. (2006) · 2006
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Bayesian Learning for Neural Networks
Neal, R. M. (2012) · 2012
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Adam: A method for stochastic optimization
Kingma, D. P. and Ba, J. (2014) · 2014
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On Bayesian inference for the M/G/1 queue with efficient MCMC sampling
Shestopaloff, A. Y. and Neal, R. M. (2014) · 2014
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MADE: Masked autoencoder for distribution estimation
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Fast ϵ \epsilon -free inference of simulation models with Bayesian conditional density estimation
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WaveNet: A generative model for raw audio
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Flexible statistical inference for mechanistic models of neural dynamics
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Masked autoregressive flow for density estimation
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Decomposition of uncertainty in bayesian deep learning for efficient and risk-sensitive learning
Depeweg, S., Hernandez-Lobato, J.-M., Doshi-Velez, F., and Udluft, S. (2018) · 2018
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Gal, Y. and Smith, L. (2018) · 2018
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Reliable uncertainty estimates in deep neural networks using noise contrastive priors
Hafner, D., Tran, D., Irpan, A., Lillicrap, T., and Davidson, J. (2018) · 2018
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Pixel recurrent neural networks
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Simple and scalable predictive uncertainty estimation using deep ensembles
Lakshminarayanan, B., Pritzel, A., and Blundell, C. (2017) · 2017
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Likelihood-free inference with emulator networks
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