Fetching the paper…
Reading the bibliography…
Sequential neural posterior estimation (SNPE) techniques have been recently proposed for dealing with simulation-based models with intractable likelihoods.
Analytical note on certain rhythmic relations in organic systems
A. J. Lotka · 1920
Earlier work this paper cites.
Exact stochastic simulation of coupled chemical reactions
D. T. Gillespie · 1977
Earlier work this paper cites.
A note on importance sampling using standardized weights
A. Kong · 1992
Earlier work this paper cites.
Mixture density networks
C. M. Bishop · 1994
Earlier work this paper cites.
Weighted average importance sampling and defensive mixture distributions
T. Hesterberg · 1995
Earlier work this paper cites.
Optimally combining sampling techniques for Monte Carlo rendering
E. Veach and L. J. Guibas · 1995
Earlier work this paper cites.
Metropolized independent sampling with comparisons to rejection sampling and importance sampling
J. S. Liu · 1996
Earlier work this paper cites.
Safe and effective importance sampling
A. Owen and Y. Zhou · 2000
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.
Numerical maximum likelihood estimation for the g-and-k and generalized g-and-h distributions
G. D. Rayner and H. L. MacGillivray · 2002
Earlier work this paper cites.
The properties of high-dimensional data spaces: implications for exploring gene and protein expression data
R. Clarke, H. W. Ressom, A. Wang, J. Xuan, M. C. Liu, E. A. Gehan, and Y. Wang · 2008
Earlier work this paper cites.
The pseudo-marginal approach for efficient Monte Carlo computations
C. Andrieu and G. O. Roberts · 2009
Earlier work this paper cites.
Particle Markov chain Monte Carlo methods
C. Andrieu, A. Doucet, and R. Holenstein · 2010
Earlier work this paper cites.
Non-linear regression models for approximate Bayesian computation
M. G. Blum and O. François · 2010
Earlier work this paper cites.
Statistical inference for noisy nonlinear ecological dynamic systems
S. N. Wood · 2010
Earlier work this paper cites.
An adaptive sequential Monte Carlo method for approximate Bayesian computation
P. Del Moral, A. Doucet, and A. Jasra · 2012
Earlier work this paper cites.
Constructing summary statistics for approximate Bayesian computation: semi-automatic approximate Bayesian computation
P. Fearnhead and D. Prangle · 2012
Earlier work this paper cites.
A kernel two-sample test
A. Gretton, K. M. Borgwardt, M. J. Rasch, B. Schölkopf, and A. Smola · 2012
Earlier work this paper cites.
Approximate Bayesian computational methods
J.-M. Marin, P. Pudlo, C. P. Robert, and R. J. Ryder · 2012
Earlier work this paper cites.
On sequential Monte Carlo, partial rejection control and approximate Bayesian computation
G. W. Peters, Y. Fan, and S. A. Sisson · 2012
Cited alongside, same era.
Expectation propagation for likelihood-free inference
S. Barthelmé and N. Chopin · 2014
Cited alongside, same era.
Adam: A method for stochastic optimization
D. P. Kingma and J. Ba · 2014
Cited alongside, same era.
On Bayesian inference for the M/G/1 queue with efficient MCMC sampling
A. Y. Shestopaloff and R. M. Neal · 2014
Cited alongside, same era.
Density estimation using real nvp
L. Dinh, J. Sohl-Dickstein, and S. Bengio · 2016
Cited alongside, same era.
Likelihood-free inference with emulator networks
J.-M. Lueckmann, G. Bassetto, T. Karaletsos, and J. H. Macke · 2019
Later among the works it cites.
Sequential neural likelihood: Fast likelihood-free inference with autoregressive flows
G. Papamakarios, D. Sterratt, and I. Murray · 2019
Later among the works it cites.
Mining gold from implicit models to improve likelihood-free inference
J. Brehmer, G. Louppe, J. Pavez, and K. Cranmer · 2020
Later among the works it cites.
The frontier of simulation-based inference
K. Cranmer, J. Brehmer, and G. Louppe · 2020
Later among the works it cites.
On contrastive learning for likelihood-free inference
C. Durkan, I. Murray, and G. Papamakarios · 2020
Later among the works it cites.
Training deep neural density estimators to identify mechanistic models of neural dynamics
P. J. Gonçalves, J.-M. Lueckmann, M. Deistler, M. Nonnenmacher, K. Öcal, G. Bassetto, C. Chintaluri, W. F. Podlaski, S. A. Haddad, T. P. Vogels, et al · 2020
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Modelling extremes using approximate Bayesian computation
R. Erhardt and S. A. Sisson · 2016
Cited alongside, same era.
Bayesian optimization for likelihood-free inference of simulator-based statistical models
M. U. Gutmann and J. Corander · 2016
Cited alongside, same era.
Revisiting classifier two-sample tests
D. Lopez-Paz and M. Oquab · 2016
Cited alongside, same era.
Fast ε \varepsilon -free inference of simulation models with Bayesian conditional density estimation
G. Papamakarios and I. Murray · 2016
Cited alongside, same era.
Estimating quantile families of loss distributions for non-life insurance modelling via l-moments
G. W. Peters, W. Y. Chen, and R. H. Gerlach · 2016
Cited alongside, same era.
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
Cited alongside, same era.
Masked autoregressive flow for density estimation
G. Papamakarios, T. Pavlakou, and I. Murray · 2017
Cited alongside, same era.
Later among the works it cites.
Likelihood-free MCMC with amortized approximate ratio estimators
J. Hermans, V. Begy, and G. Louppe · 2020
Later among the works it cites.
Normalizing flows: An introduction and review of current methods
I. Kobyzev, S. J. Prince, and M. A. Brubaker · 2020
Later among the works it cites.
Likelihood-free approximate gibbs sampling
G. Rodrigues, D. J. Nott, and S. A. Sisson · 2020
Later among the works it cites.
A trust crisis in simulation-based inference? your posterior approximations can be unfaithful
J. Hermans, A. Delaunoy, F. Rozet, A. Wehenkel, V. Begy, and G. Louppe · 2021
Later among the works it cites.
Benchmarking simulation-based inference
J.-M. Lueckmann, J. Boelts, D. Greenberg, P. Goncalves, and J. Macke · 2021
Later among the works it cites.
Truncated proposals for scalable and hassle-free simulation-based inference
M. Deistler, P. J. Goncalves, and J. H. Macke · 2022
Later among the works it cites.
Variational methods for simulation-based inference
M. Glöckler, M. Deistler, and J. H. Macke · 2022
Later among the works it cites.
Unbiased MLMC-based variational Bayes for likelihood-free inference
Z. He, Z. Xu, and X. Wang · 2022
Later among the works it cites.
Contrastive Neural Ratio Estimation
B. K. Miller, C. Weniger, and P. Forré · 2022
Later among the works it cites.
Likelihood-free inference by ratio estimation
O. Thomas, R. Dutta, J. Corander, S. Kaski, and M. U. Gutmann · 2022
Later among the works it cites.
Amortized Bayesian inference on generative dynamical network models of epilepsy using deep neural density estimators
M. Hashemi, A. N. Vattikonda, J. Jha, V. Sip, M. M. Woodman, F. Bartolomei, and V. K. Jirsa · 2023
Closest in time.
A practical guide to statistical distances for evaluating generative models in science
S. Bischoff, A. Darcher, M. Deistler, R. Gao, F. Gerken, M. Gloeckler, L. Haxel, J. Kapoor, J. K. Lappalainen, J. H. Macke, et al · 2024
Closest in time.