Fetching the paper…
Reading the bibliography…
We present Sequential Neural Likelihood (SNL), a new method for Bayesian inference in simulator models, where the likelihood is intractable but simulating data from the model is possible.
A quantitative description of membrane current and its application to conduction and excitation in nerve
A. L. Hodgkin and A. F. Huxley · 1952
Earlier work this paper cites.
Exact stochastic simulation of coupled chemical reactions
D. T. Gillespie · 1977
Earlier work this paper cites.
Indirect inference
C. Gouriéroux, A. Monfort, and E. Renault · 1993
Earlier work this paper cites.
Probabilistic inference using Markov chain Monte Carlo methods
R. M. Neal · 1993
Earlier work this paper cites.
Mixture density networks
C. M. Bishop · 1994
Earlier work this paper cites.
Population growth of human Y chromosomes: a study of Y chromosome microsatellites
J. K. Pritchard, M. T. Seielstad, A. Perez-Lezaun, and M. W. Feldman · 1999
Earlier work this paper cites.
Global structure, robustness, and modulation of neuronal models
M. S. Goldman, J. Golowasch, E. Marder, and L. F. Abbott · 2001
Earlier work this paper cites.
Recognition networks for approximate inference in BN20 networks
Q. Morris · 2001
Earlier work this paper cites.
Approximate Bayesian computation in population genetics
M. A. Beaumont, W. Zhang, and D. J. Balding · 2002
Earlier work this paper cites.
Ion regulation in the brain: implications for pathophysiology
G. G. Somjen · 2002
Earlier work this paper cites.
Geant4—a simulation toolkit
S. Agostinelli et al · 2003
Earlier work this paper cites.
Markov chain Monte Carlo without likelihoods
P. Marjoram, J. Molitor, V. Plagnol, and S. Tavaré · 2003
Earlier work this paper cites.
Slice sampling
R. M. Neal · 2003
Earlier work this paper cites.
Risks for the long run: A potential resolution of asset pricing puzzles
R. Bansal and A. Yaron · 2004
Earlier work this paper cites.
The NEURON Book
T. Carnevale and M. Hines · 2006
Earlier work this paper cites.
Vision as Bayesian inference: analysis by synthesis?
A. Yuille and D. Kersten · 2006
Earlier work this paper cites.
Using likelihood-free inference to compare evolutionary dynamics of the protein networks of H. pylori and P. falciparum
O. Ratmann, O. Jørgensen, T. Hinkley, M. Stumpf, S. Richardson, and C. Wiuf · 2007
Earlier work this paper cites.
Sequential Monte Carlo without likelihoods
S. A. Sisson, Y. Fan, and M. M. Tanaka · 2007
Earlier work this paper cites.
Minimal Hodgkin–Huxley type models for different classes of cortical and thalamic neurons
M. Pospischil, M. Toledo-Rodriguez, C. Monier, Z. Piwkowska, T. Bal, Y. Frégnac, H. Markram, and A. Destexhe · 2008
Earlier work this paper cites.
A brief introduction to PYTHIA 8.1
T. Sjöstrand, S. Mrenna, and P. Skands · 2008
Earlier work this paper cites.
Graphical models, exponential families, and variational inference
M. J. Wainwright and M. I. Jordan · 2008
Earlier work this paper cites.
Adaptive approximate Bayesian computation
M. A. Beaumont, J.-M. Cornuet, J.-M. Marin, and C. P. Robert · 2009
Earlier work this paper cites.
Approximate Bayesian computation scheme for parameter inference and model selection in dynamical systems
T. Toni, D. Welch, N. Strelkowa, A. Ipsen, and M. P. H. Stumpf · 2009
Earlier work this paper cites.
Approximate Bayesian computation in evolution and ecology
M. A. Beaumont · 2010
Earlier work this paper cites.
Approximate Bayesian computation: A nonparametric perspective
M. G. B. Blum · 2010
Earlier work this paper cites.
Non-linear regression models for approximate Bayesian computation
M. G. B. Blum and O. François · 2010
Earlier work this paper cites.
Bayesian computation and model selection without likelihoods
C. Leuenberger and D. Wegmann · 2010
Earlier work this paper cites.
Statistical inference for noisy nonlinear ecological dynamic systems
S. N. Wood · 2010
Cited alongside, same era.
MCMC using Hamiltonian dynamics
R. M. Neal · 2011
Cited alongside, same era.
Likelihood-free Markov chain Monte Carlo
S. A. Sisson and Y. Fan · 2011
Cited alongside, same era.
Principles of Computational Modelling in Neuroscience
D. C. Sterratt, B. Graham, A. Gillies, and D. Willshaw · 2011
Cited alongside, same era.
Stochastic Modelling for Systems Biology, Second Edition
D. J. Wilkinson · 2011
Cited alongside, same era.
A kernel two-sample test
A. Gretton, K. M. Borgwardt, M. J. Rasch, B. Schölkopf, and A. Smola · 2012
Cited alongside, same era.
Approximate Bayesian computational methods
Bayesian optimization for likelihood-free inference of simulator-based statistical models
M. U. Gutmann and J. Corander · 2016
Later among the works it cites.
On the identifiability of transmission dynamic models for infectious diseases
J. Lintusaari, M. U. Gutmann, S. Kaski, and J. Corander · 2016
Later among the works it cites.
A. Moreno, T. Adel, E. Meeds, J. M. Rehg, and M. Welling · 2016
Later among the works it cites.
Inference networks for sequential Monte Carlo in graphical models
B. Paige and F. Wood · 2016
Later among the works it cites.
Fast ϵ \epsilon -free inference of simulation models with Bayesian conditional density estimation
G. Papamakarios and I. Murray · 2016
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
J.-M. Marin, P. Pudlo, C. P. Robert, and R. J. Ryder · 2012
Cited alongside, same era.
Likelihood-free inference in cosmology: Potential for the estimation of luminosity functions
C. M. Schafer and P. E. Freeman · 2012
Cited alongside, same era.
Approximate Bayesian Computation via regression density estimation
Y. Fan, D. J. Nott, and S. A. Sisson · 2013
Cited alongside, same era.
Approximate Bayesian image interpretation using generative probabilistic graphics programs
V. K. Mansinghka, T. D. Kulkarni, Y. N. Perov, and J. B. Tenenbaum · 2013
Cited alongside, same era.
High-dimensional density ratio estimation with extensions to approximate likelihood computation
R. Izbicki, A. Lee, and C. Schafer · 2014
Cited alongside, same era.
GPS-ABC: Gaussian process surrogate approximate Bayesian computation
E. Meeds and M. Welling · 2014
Cited alongside, same era.
M. L. Casado, A. G. Baydin, D. M. Rubio, T. A. Le, F. Wood, L. Heinrich, G. Louppe, K. Cranmer, K. Ng, W. Bhimji, and Prabhat · 2017
Later among the works it cites.
Probabilistic programs for inferring the goals of autonomous agents
M. F. Cusumano-Towner, A. Radul, D. Wingate, and V. K. Mansinghka · 2017
Later among the works it cites.
Density estimation using Real NVP
L. Dinh, J. Sohl-Dickstein, and S. Bengio · 2017
Later among the works it cites.
Asymptotically exact inference in differentiable generative models
M. M. Graham and A. J. Storkey · 2017
Later among the works it cites.
Inference compilation and universal probabilistic programming
T. A. Le, A. G. Baydin, and F. Wood · 2017
Later among the works it cites.
Extending approximate Bayesian computation methods to high dimensions via a Gaussian copula model
J. Li, D. J. Nott, Y. Fan, and S. A. Sisson · 2017
Later among the works it cites.
Fundamentals and recent developments in approximate Bayesian computation
J. Lintusaari, M. U. Gutmann, R. Dutta, S. Kaski, and J. Corander · 2017
Later among the works it cites.
Flexible statistical inference for mechanistic models of neural dynamics
J.-M. Lueckmann, P. J. Goncalves, G. Bassetto, K. Öcal, M. Nonnenmacher, and J. H. Macke · 2017
Later among the works it cites.
Masked autoregressive flow for density estimation
G. Papamakarios, T. Pavlakou, and I. Murray · 2017
Later among the works it cites.
Vision-as-inverse-graphics: Obtaining a rich 3D explanation of a scene from a single image
L. Romaszko, C. K. Williams, P. Moreno, and P. Kohli · 2017
Later among the works it cites.
Massive optimal data compression and density estimation for scalable, likelihood-free inference in cosmology
J. Alsing, B. Wandelt, and S. Feeney · 2018
Closest in time.
Bootstrapped synthetic likelihood
R. G. Everitt · 2018
Closest in time.
An extended empirical saddlepoint approximation for intractable likelihoods
M. Fasiolo, S. N. Wood, F. Hartig, and M. V. Bravington · 2018
Closest in time.
Likelihood-free inference via classification
M. U. Gutmann, R. Dutta, S. Kaski, and J. Corander · 2018
Closest in time.
Efficient acquisition rules for model-based approximate Bayesian computation
M. Järvenpää, M. U. Gutmann, A. Pleska, A. Vehtari, and P. Marttinen · 2018
Closest in time.
Likelihood-free inference with emulator networks
J.-M. Lueckmann, G. Bassetto, T. Karaletsos, and J. H. Macke · 2018
Closest in time.
Variational Bayes with synthetic likelihood
V. M. H. Ong, D. J. Nott, M.-N. Tran, S. A. Sisson, and C. C. Drovandi · 2018
Closest in time.
Bayesian synthetic likelihood
L. F. Price, C. C. Drovandi, A. Lee, and D. J. Nott · 2018
Closest in time.
Likelihood-free inference with an improved cross-entropy estimator
M. Stoye, J. Brehmer, G. Louppe, J. Pavez, and K. Cranmer · 2018
Closest in time.
Validating Bayesian inference algorithms with simulation-based calibration
S. Talts, M. Betancourt, D. Simpson, A. Vehtari, and A. Gelman · 2018
Closest in time.
Parallel WaveNet: Fast high-fidelity speech synthesis
A. van den Oord et al · 2018
Closest in time.