Fetching the paper…
Reading the bibliography…
Simulation-based inference has been popular for amortized Bayesian computation.
On the composition of elementary errors
Cramér, H. (1928) · 1928
Earlier work this paper cites.
Scoring rules for continuous probability distributions
Matheson, J. E. and Winkler, R. L. (1976) · 1976
Earlier work this paper cites.
The well-calibrated Bayesian
Dawid, A. P. (1982) · 1982
Earlier work this paper cites.
Elements of Information Theory
Cover, T. M. and Thomas, J. A. (1991) · 1991
Earlier work this paper cites.
Stacked generalization
Wolpert, D. H. (1992) · 1992
Earlier work this paper cites.
Stacked regressions
Breiman, L. (1996) · 1996
Earlier work this paper cites.
Asymptotic Statistics
van der Vaart, A. W. (1998) · 1998
Earlier work this paper cites.
Bayesian model averaging: a tutorial
Hoeting, J. A., Madigan, D., Raftery, A. E., and Volinsky, C. T. (1999) · 1999
Earlier work this paper cites.
Validation of software for Bayesian models using posterior quantiles
Cook, S. R., Gelman, A., and Rubin, D. B. (2006) · 2006
Earlier work this paper cites.
Strictly proper scoring rules, prediction, and estimation
Gneiting, T. and Raftery, A. E. (2007) · 2007
Earlier work this paper cites.
A survey of bayesian predictive methods for model assessment, selection and comparison
Vehtari, A. and Ojanen, J. (2012) · 2012
Earlier work this paper cites.
Bayesian model averaging in the M-open framework
Clyde, M. and Iversen, E. S. (2013) · 2013
Earlier work this paper cites.
Adam: A method for stochastic optimization
Kingma, D. P. and Ba, J. (2015) · 2015
Earlier work this paper cites.
A Bayes interpretation of stacking for ℳ \mathcal{M} -complete and ℳ \mathcal{M} -open settings
Le, T. and Clarke, B. (2017) · 2017
Earlier work this paper cites.
Masked autoregressive flow for density estimation
Papamakarios, G., Pavlakou, T., and Murray, I. (2017) · 2017
Cited alongside, same era.
Merging MCMC subposteriors through gaussian-process approximations
Nemeth, C. and Sherlock, C. (2018) · 2018
Cited alongside, same era.
Validating Bayesian inference algorithms with simulation-based calibration
Talts, S., Betancourt, M., Simpson, D., Vehtari, A., and Gelman, A. (2018) · 2018
Cited alongside, same era.
Using stacking to average Bayesian predictive distributions (with discussion)
Yao, Y., Vehtari, A., Simpson, D., and Gelman, A. (2018) · 2018
Cited alongside, same era.
Neural spline flows
Durkan, C., Bekasov, A., Murray, I., and Papamakarios, G. (2019) · 2019
Cited alongside, same era.
Automatic posterior transformation for likelihood-free inference
Real-time gravitational wave science with neural posterior estimation
Dax, M., Green, S. R., Gair, J., Macke, J. H., Buonanno, A., and Schölkopf, B. (2021) · 2021
Later among the works it cites.
An adaptive-MCMC scheme for setting trajectory lengths in Hamiltonian Monte Carlo
Hoffman, M., Radul, A., and Sountsov, P. (2021) · 2021
Later among the works it cites.
Benchmarking simulation-based inference
Lueckmann, J.-M., Boelts, J., Greenberg, D., Goncalves, P., and Macke, J. (2021) · 2021
Later among the works it cites.
Assessment and adjustment of approximate inference algorithms using the law of total variance
Yu, X., Nott, D. J., Tran, M.-N., and Klein, N. (2021) · 2021
Later among the works it cites.
GATSBI: Generative adversarial training for simulation-based inference
Ramesh, P., Lueckmann, J.-M., Boelts, J., Tejero-Cantero, Á., Greenberg, D. S., Gonçalves, P. J., and Macke, J. H. (2022) · 2022
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Greenberg, D., Nonnenmacher, M., and Macke, J. (2019) · 2019
Cited alongside, same era.
Sequential neural likelihood: Fast likelihood-free inference with autoregressive flows
Papamakarios, G., Sterratt, D., and Murray, I. (2019) · 2019
Cited alongside, same era.
The frontier of simulation-based inference
Cranmer, K., Brehmer, J., and Louppe, G. (2020) · 2020
Cited alongside, same era.
Training deep neural density estimators to identify mechanistic models of neural dynamics
Gonçalves, P. J., Lueckmann, J.-M., Deistler, M., Nonnenmacher, M., Öcal, K., Bassetto, G., Chintaluri, C., Podlaski, W. F., Haddad, S. A., and Vogels, T. P. (2020) · 2020
Cited alongside, same era.
Solving high-dimensional parameter inference: marginal posterior densities & moment networks
Jeffrey, N. and Wandelt, B. D. (2020) · 2020
Cited alongside, same era.
Embarrassingly parallel MCMC using deep invertible transformations
Mesquita, D., Blomstedt, P., and Kaski, S. (2020) · 2020
Cited alongside, same era.
SBI: A toolkit for simulation-based inference
Tejero-Cantero, A., Boelts, J., Deistler, M., Lueckmann, J.-M., Durkan, C., Gonçalves, P. J., Greenberg, D. S., and Macke, J. H. (2020) · 2020
Cited alongside, same era.
Stacking for non-mixing Bayesian computations: The curse and blessing of multimodal posteriors
Yao, Y., Vehtari, A., and Gelman, A. (2022) · 2022
Later among the works it cites.
A forward modeling approach to analyzing galaxy clustering with simbig
Hahn, C., Eickenberg, M., Ho, S., Hou, J., Lemos, P., Massara, E., Modi, C., Moradinezhad Dizgah, A., Régaldo-Saint Blancard, B., and Abidi, M. M. (2023) · 2023
Closest in time.
Simulation-based inference with waldo: Perfectly calibrated confidence regions using any prediction or posterior estimation algorithm
Masserano, L., Dorigo, T., Izbicki, R., Kuusela, M., and Lee, A. B. (2023) · 2023
Closest in time.
Simulation-based calibration checking for Bayesian computation: The choice of test quantities shapes sensitivity
Modrák, M., Moon, A. H., Kim, S., Bürkner, P., Huurre, N., Faltejsková, K., Gelman, A., and Vehtari, A. (2023) · 2023
Closest in time.
SimBIG: Galaxy clustering analysis with the wavelet scattering transform
Régaldo-Saint Blancard, B., Hahn, C., Ho, S., Hou, J., Lemos, P., Massara, E., Modi, C., Moradinezhad Dizgah, A., Parker, L., Yao, Y., and Eickenberg, M. (2023) · 2023
Closest in time.
Yao, Y. and Domke, J. (2023) · 2023
Closest in time.
The generalized multiplicative gradient method and its convergence rate analysis
Zhao, R. (2023) · 2023
Closest in time.
Stacking as a way of life: A general framework for combining predictive distributions
Yao, Y., Ouyang, J., and Buja, A. (2024) · 2024
Closest in time.