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Full Bayesian posteriors are rarely analytically tractable, which is why real-world Bayesian inference heavily relies on approximate techniques.
The sum of log-normal probability distributions in scatter transmission systems
L. Fenton · 1960
Earlier work this paper cites.
Monte Carlo sampling methods using Markov chains and their applications
W. K. Hastings · 1970
Earlier work this paper cites.
Monte Carlo methods of inference for implicit statistical models
P. J. Diggle and R. J. Gratton · 1984
Earlier work this paper cites.
Stochastic relaxation, Gibbs distributions, and the Bayesian restoration of images
S. Geman and D. Geman · 1984
Earlier work this paper cites.
Compatible conditional distributions
B. C. Arnold and S. J. Press · 1989
Earlier work this paper cites.
Sequential updating of conditional probabilities on directed graphical structures
D. J. Spiegelhalter and S. L. Lauritzen · 1990
Earlier work this paper cites.
Explaining the Gibbs sampler
G. Casella and E. I. George · 1992
Earlier work this paper cites.
Inference from iterative simulation using multiple sequences
A. Gelman and D. B. Rubin · 1992
Earlier work this paper cites.
A practical Bayesian framework for backpropagation networks
D. J. C. MacKay · 1992
Earlier work this paper cites.
Keeping the neural networks simple by minimizing the description length of the weights
G. E. Hinton and D. van Camp · 1993
Earlier work this paper cites.
Markov chains
J. R. Norris and J. R. Norris · 1998
Earlier work this paper cites.
An introduction to variational methods for graphical models
M. Jordan, Z. Ghahramani, T. Jaakkola, and L. Saul · 1999
Earlier work this paper cites.
Conditionally specified distributions: An introduction (with comments and a rejoinder by the authors)
B. C. Arnold, E. Castillo, and J. M. Sarabia · 2001
Earlier work this paper cites.
Dependency networks for inference, collaborative filtering, and data visualization
D. Heckerman, D. M. Chickering, C. Meek, R. Rounthwaite, and C. Kadie · 2001
Earlier work this paper cites.
Expectation propagation for approximate Bayesian inference
T. P. Minka · 2001
Earlier work this paper cites.
Exact and near compatibility of discrete conditional distributions
B. C. Arnold, E. Castillo, and J. M. Sarabia · 2002
Earlier work this paper cites.
Getting it right: Joint distribution tests of posterior simulators
J. Geweke · 2004
Earlier work this paper cites.
Pattern Recognition and Machine Learning
C. M. Bishop · 2006
Earlier work this paper cites.
Validation of software for Bayesian models using posterior quantiles
S. R. Cook, A. Gelman, and D. B. Rubin · 2006
Cited alongside, same era.
Fully conditional specification in multivariate imputation
S. Van Buuren, J. P. Brand, C. G. Groothuis-Oudshoorn, and D. B. Rubin · 2006
Cited alongside, same era.
Approximate Bayesian inference for latent Gaussian models by using integrated nested Laplace approximations
H. Rue, S. Martino, and N. Chopin · 2009
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A simple algorithm for checking compatibility among discrete conditional distributions
K.-L. Kuo and Y. J. Wang · 2011
Cited alongside, same era.
Two problems with variational expectation maximisation for time-series models
R. E. Turner and M. Sahani · 2011
Cited alongside, same era.
Stochastic variational inference
M. D. Hoffman, D. M. Blei, C. Wang, and J. Paisley · 2013
Automatic differentiation variational inference
A. Kucukelbir, D. Tran, R. Ranganath, A. Gelman, and D. M. Blei · 2017
Later among the works it cites.
Exactly and almost compatible joint distributions for high-dimensional discrete conditional distributions
K.-L. Kuo, C.-C. Song, and T. J. Jiang · 2017
Later among the works it cites.
Bayesian computing with inla: A review
H. Rue, A. Riebler, S. H. Sørbye, J. B. Illian, D. P. Simpson, and F. K. Lindgren · 2017
Later among the works it cites.
Recalibration: A post-processing method for approximate Bayesian computation
G. Rodrigues, D. Prangle, and S. Sisson · 2018
Later among the works it cites.
Handbook of approximate Bayesian computation
S. A. Sisson, Y. Fan, and M. Beaumont · 2018
Later among the works it cites.
Validating Bayesian inference algorithms with simulation-based calibration
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Behavior of the Gibbs sampler when conditional distributions are potentially incompatible
S.-H. Chen and E. Ip · 2014
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The no-u-turn sampler: adaptively setting path lengths in Hamiltonian Monte Carlo
M. D. Hoffman and A. Gelman · 2014
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Joint modelling rationale for chained equations
R. A. Hughes, I. R. White, S. R. Seaman, J. R. Carpenter, K. Tilling, and J. A. Sterne · 2014
Cited alongside, same era.
Diagnostic tools for approximate Bayesian computation using the coverage property
D. Prangle, M. G. B. Blum, G. Popovic, and S. A. Sisson · 2014
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Black box variational inference
R. Ranganath, S. Gerrish, and D. Blei · 2014
Cited alongside, same era.
Study of incompatibility or near compatibility of bivariate discrete conditional probability distributions through divergence measures
I. Ghosh and N. Balakrishnan · 2015
Cited alongside, same era.
S. Talts, M. Betancourt, D. Simpson, A. Vehtari, and A. Gelman · 2018
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Yes, but did it work?: Evaluating variational inference
Y. Yao, A. Vehtari, D. Simpson, and A. Gelman · 2018
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Approximate Bayesian computation
M. A. Beaumont · 2019
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Pseudo-Gibbs sampler for discrete conditional distributions
K.-L. Kuo and Y. J. Wang · 2019
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Optimal compromise between incompatible conditional probability distributions, with application to Objective Bayesian Kriging
J. Muré · 2019
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Composable effects for flexible and accelerated probabilistic programming in numpyro
D. Phan, N. Pradhan, and M. Jankowiak · 2019
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Validated variational inference via practical posterior error bounds
J. Huggins, M. Kasprzak, T. Campbell, and T. Broderick · 2020
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Duality between approximate Bayesian methods and prior robustness
C. Joshi and F. Ruggeri · 2020
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Convergence diagnostics for Markov chain Monte Carlo
V. Roy · 2020
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Distortion estimates for approximate Bayesian inference
H. Xing, G. Nicholls, and J. (Kate) Lee · 2020
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Laplace redux-effortless Bayesian deep learning
E. Daxberger, A. Kristiadi, A. Immer, R. Eschenhagen, M. Bauer, and P. Hennig · 2021
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An easy to interpret diagnostic for approximate inference: Symmetric divergence over simulations
J. Domke · 2021
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Assessment and adjustment of approximate inference algorithms using the law of total variance
X. Yu, D. J. Nott, M.-N. Tran, and N. Klein · 2021
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