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Simulation-based inference (SBI) methods such as approximate Bayesian computation (ABC), synthetic likelihood, and neural posterior estimation (NPE) rely on simulating statistics to infer parameters of intractable likelihood models.
Conditional density estimation with neural networks: Best practices and benchmarks
Rothfuss, J., Ferreira, F., Walther, S., and Ulrich, M. (2019) · 1903
Earlier work this paper cites.
Generalized variational inference: Three arguments for deriving new posteriors
Knoblauch, J., Jewson, J., and Damoulas, T. (2019) · 1904
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Approximate Bayesian computation with domain expert in the loop
Bharti, A., Filstroff, L., and Kaski, S. (2022b) · 1905
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Statistical inference for generative models with maximum mean discrepancy
Briol, F.-X., Barp, A., Duncan, A. B., and Girolami, M. (2019) · 1906
Earlier work this paper cites.
Finite sample properties of parametric MMD estimation: robustness to misspecification and dependence
Chérief-Abdellatif, B.-E. and Alquier, P. (2021) · 1912
Earlier work this paper cites.
A statistical model of urban multipath propagation
Turin, G. L., Clapp, F. D., Johnston, T. L., Fine, S. B., and Lavry, D. (1972) · 1972
Earlier work this paper cites.
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Diggle, P. J. and Gratton, R. J. (1984) · 1984
Earlier work this paper cites.
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Earlier work this paper cites.
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Alquier, P. and Gerber, M. (2021) · 2006
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Robust approximate bayesian computation: An adjustment approach
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Earlier work this paper cites.
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Non-linear regression models for approximate Bayesian computation
Blum, M. G. B. and François, O. (2010) · 2010
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Wood, S. N. (2010) · 2010
Earlier work this paper cites.
Generalized posteriors in approximate bayesian computation
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Earlier work this paper cites.
Constructing summary statistics for approximate Bayesian computation: semi-automatic approximate bayesian computation
Fearnhead, P. and Prangle, D. (2012) · 2012
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A kernel two-sample test
Gretton, A., Borgwardt, K., Rasch, M. J., and Scholkopf, B. (2012) · 2012
Earlier work this paper cites.
The Safe Bayesian
Grünwald, P. (2012) · 2012
Earlier work this paper cites.
A comparative review of dimension reduction methods in approximate Bayesian computation
Blum, M. G. B., Nunes, M. A., Prangle, D., and Sisson, S. A. (2013) · 2013
Earlier work this paper cites.
A statistical spatio-temporal radio channel model for large indoor environments at 60 and 70 ghz
Haneda, K., Järveläinen, J., Karttunen, A., Kyrö, M., and Putkonen, J. (2015) · 2015
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K2-ABC: approximate Bayesian computation with kernel embeddings
Park, M., Jitkrittum, W., and Sejdinovic, D. (2015) · 2015
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Reliable ABC model choice via random forests
Pudlo, P., Marin, J.-M., Estoup, A., Cornuet, J.-M., Gautier, M., and Robert, C. P. (2015) · 2015
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A general framework for updating belief distributions
Bissiri, P. G., Holmes, C. C., and Walker, S. G. (2016) · 2016
Earlier work this paper cites.
Modeling the polarimetric mm-wave propagation channel using censored measurements
Gustafson, C., Bolin, D., and Tufvesson, F. (2016) · 2016
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DR-ABC: Approximate Bayesian computation with kernel-based distribution regression
Mitrovic, J., Sejdinovic, D., and Teh, Y.-W. (2016) · 2016
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Fast ϵ \epsilon -free inference of simulation models with bayesian conditional density estimation
Papamakarios, G. and Murray, I. (2016) · 2016
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Learning summary statistic for approximate Bayesian computation via deep neural network
Jiang, B., yu Wu, T., Zheng, C., and Wong, W. (2017) · 2017
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Fundamentals and recent developments in approximate Bayesian computation
Lintusaari, J., Gutmann, M. U., Dutta, R., Kaski, S., and Corander, J. (2017) · 2017
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Flexible statistical inference for mechanistic models of neural dynamics
Lueckmann, J.-M., Goncalves, P. J., Bassetto, G., Öcal, K., Nonnenmacher, M., and Macke, J. H. (2017) · 2017
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Kernel mean embedding of distributions: A review and beyond
Muandet, K., Fukumizu, K., Sriperumbudur, B., and Schölkopf, B. (2017) · 2017
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Masked autoregressive flow for density estimation
Papamakarios, G., Pavlakou, T., and Murray, I. (2017) · 2017
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Deep sets
Zaheer, M., Kottur, S., Ravanbakhsh, S., Poczos, B., Salakhutdinov, R. R., and Smola, A. J. (2017) · 2017
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A likelihood-free inference framework for population genetic data using exchangeable neural networks
Generalized bayesian likelihood-free inference using scoring rules estimators
Pacchiardi, L. and Dutta, R. (2021) · 2021
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Bayesian neural networks with maximum mean discrepancy regularization
Pomponi, J., Scardapane, S., and Uncini, A. (2021) · 2021
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Detecting model misspecification in amortized bayesian inference with neural networks
Schmitt, M., Bürkner, P.-C., Köthe, U., and Radev, S. T. (2021) · 2021
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Sequential neural posterior and likelihood approximation
Wiqvist, S., Frellsen, J., and Picchini, U. (2021) · 2021
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Learning Summary Statistics for Bayesian Inference with Autoencoders
Albert, C., Ulzega, S., Ozdemir, F., Perez-Cruz, F., and Mira, A. (2022) · 2022
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Chan, J., Perrone, V., Spence, J., Jenkins, P., Mathieson, S., and Song, Y. (2018) · 2018
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Dynamic likelihood-free inference via ratio estimation (DIRE)
Dinev, T. and Gutmann, M. U. (2018) · 2018
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Bayesian synthetic likelihood
Price, L. F., Drovandi, C. C., Lee, A., and Nott, D. J. (2018) · 2018
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Deep generative models of genetic variation capture the effects of mutations
Riesselman, A. J., Ingraham, J. B., and Marks, D. S. (2018) · 2018
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Handbook of Approximate Bayesian Computation
Sisson, S. A. (2018) · 2018
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Approximate Bayesian computation
Beaumont, M. A. (2019) · 2019
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Automatic posterior transformation for likelihood-free inference
Greenberg, D., Nonnenmacher, M., and Macke, J. (2019) · 2019
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Investigating the impact of model misspecification in neural simulation-based inference
Cannon, P., Ward, D., and Schmon, S. M. (2022) · 2022
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Group equivariant neural posterior estimation
Dax, M., Green, S. R., Gair, J., Deistler, M., Schölkopf, B., and Macke, J. H. (2022) · 2022
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Towards reliable simulation-based inference with balanced neural ratio estimation
Delaunoy, A., Hermans, J., Rozet, F., Wehenkel, A., and Louppe, G. (2022) · 2022
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Robust bayesian inference for simulator-based models via the mmd posterior bootstrap
Dellaporta, C., Knoblauch, J., Damoulas, T., and Briol, F.-X. (2022) · 2022
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Normalizing flows for likelihood-free inference with fusion simulations
Furia, C. S. and Churchill, R. M. (2022) · 2022
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Score modeling for simulation-based inference
Geffner, T., Papamakarios, G., and Mnih, A. (2022) · 2022
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Variational methods for simulation-based inference
Glöckler, M., Deistler, M., and Macke, J. H. (2022) · 2022
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Concentration and robustness of discrepancy-based ABC via Rademacher complexity
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Likelihood-free inference with generative neural networks via scoring rule minimization
Pacchiardi, L. and Dutta, R. (2022) · 2022
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ABC of the future
Pesonen, H., Simola, U., Köhn-Luque, A., Vuollekoski, H., Lai, X., Frigessi, A., Kaski, S., Frazier, D. T., Maneesoonthorn, W., Martin, G. M., and Corander, J. (2022) · 2022
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Bayesflow: Learning complex stochastic models with invertible neural networks
Radev, S. T., Mertens, U. K., Voss, A., Ardizzone, L., and Köthe, U. (2022) · 2022
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GATSBI: Generative adversarial training for simulation-based inference
Ramesh, P., Lueckmann, J.-M., Boelts, J., Tejero-Cantero, Á., Greenberg, D. S., Goncalves, P. J., and Macke, J. H. (2022) · 2022
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Sharrock, L., Simons, J., Liu, S., and Beaumont, M. (2022) · 2022
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Likelihood-free inference by ratio estimation
Thomas, O., Dutta, R., Corander, J., Kaski, S., and Gutmann, M. U. (2022) · 2022
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Robust neural posterior estimation and statistical model criticism
Ward, D., Cannon, P., Beaumont, M., Fasiolo, M., and Schmon, S. M. (2022) · 2022
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Approximate bayesian estimation of parameters of an agent-based model in epidemiology
Zbair, M., Qaffou, A., and Hilal, K. (2022) · 2022
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Optimally-weighted estimators of the maximum mean discrepancy for likelihood-free inference
Bharti, A., Naslidnyk, M., Key, O., Kaski, S., and Briol, F.-X. (2023) · 2023
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Simulation-based inference of single-molecule force spectroscopy
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Misspecification-robust sequential neural likelihood
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Neural posterior estimation for exoplanetary atmospheric retrieval
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Bayesian data selection
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