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Recent advances in probabilistic modelling have led to a large number of simulation-based inference algorithms which do not require numerical evaluation of likelihoods.
Efficient bayesian synthetic likelihood with whitening transformations
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Efficient bayesian synthetic likelihood with whitening transformations
Priddle, J. W., S. A. Sisson, and C. Drovandi 2019 · 1909
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Distilling importance sampling
Prangle, D. 2019 · 1910
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Normalizing flows for probabilistic modeling and inference
Papamakarios, G., E. Nalisnick, D. J. Rezende, S. Mohamed, and B. Lakshminarayanan 2019a · 1912
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Normalizing flows for probabilistic modeling and inference
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Analytical note on certain rhythmic relations in organic systems
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Using the sir algorithm to simulate posterior distributions
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Indirect inference
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Inferring coalescence times from dna sequence data
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Nonparametric input estimation in physiological systems: problems, methods, and case studies
De Nicolao, G., G. Sparacino, and C. Cobelli 1997 · 1997
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Inferring coalescence times from dna sequence data
Tavaré, S., D. J. Balding, R. C. Griffiths, and P. Donnelly 1997 · 1997
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Population growth of human y chromosomes: a study of y chromosome microsatellites
Pritchard, J. K., M. T. Seielstad, A. Perez-Lezaun, and M. W. Feldman 1999 · 1999
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Population growth of human y chromosomes: a study of y chromosome microsatellites
Pritchard, J. K., M. T. Seielstad, A. Perez-Lezaun, and M. W. Feldman 1999 · 1999
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The aurora experimental framework for the performance evaluation of speech recognition systems under noisy conditions
Hirsch, H.-G. and D. Pearce 2000 · 2000
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Random forests
Breiman, L. 2001 · 2001
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Planning as inference in epidemiological models
Wood, F., A. Warrington, S. Naderiparizi, C. Weilbach, V. Masrani, W. Harvey, A. Scibior, B. Beronov, and A. Nasseri 2020 · 2003
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Slice sampling
Neal, R. M. 2003 · 2003
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On multivariate goodness-of-fit and two-sample testing
Friedman, J. 2004 · 2004
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Adaptive mcmc for synthetic likelihoods and correlated synthetic likelihoods
Picchini, U., U. Simola, and J. Corander 2020 · 2004
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Bayesian Data Analysis
Gelman, A., J. B. Carlin, H. S. Stern, and D. B. Rubin 2004 · 2004
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Modern computational approaches for analysing molecular genetic variation data
Marjoram, P. and S. Tavaré 2006 · 2006
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Modern computational approaches for analysing molecular genetic variation data
Marjoram, P. and S. Tavaré 2006 · 2006
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Using likelihood-free inference to compare evolutionary dynamics of the protein networks of h. pylori and p. falciparum
Ratmann, O., O. Jørgensen, T. Hinkley, M. Stumpf, S. Richardson, and C. Wiuf 2007 · 2007
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Sequential monte carlo without likelihoods
Sisson, S. A., Y. Fan, and M. M. Tanaka 2007 · 2007
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Sequential monte carlo without likelihoods
Sisson, S. A., Y. Fan, and M. M. Tanaka 2007 · 2007
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Adaptive approximate bayesian computation
Beaumont, M. A., J.-M. Cornuet, J.-M. Marin, and C. P. Robert 2009 · 2009
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Approximate bayesian computation scheme for parameter inference and model selection in dynamical systems
Toni, T., D. Welch, N. Strelkowa, A. Ipsen, and M. P. Stumpf 2009 · 2009
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Adaptive approximate bayesian computation
Beaumont, M. A., J.-M. Cornuet, J.-M. Marin, and C. P. Robert 2009 · 2009
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Approximate bayesian computation scheme for parameter inference and model selection in dynamical systems
Toni, T., D. Welch, N. Strelkowa, A. Ipsen, and M. P. Stumpf 2009 · 2009
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Non-linear regression models for approximate bayesian computation
Blum, M. G. and O. François 2010 · 2010
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Statistical inference for noisy nonlinear ecological dynamic systems
Wood, S. N. 2010 · 2010
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Non-linear regression models for approximate bayesian computation
Blum, M. G. and O. François 2010 · 2010
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Statistical inference for noisy nonlinear ecological dynamic systems
Wood, S. N. 2010 · 2010
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Scikit-learn: Machine learning in Python
Pedregosa, F., G. Varoquaux, A. Gramfort, V. Michel, B. Thirion, O. Grisel, M. Blondel, P. Prettenhofer, R. Weiss, V. Dubourg, J. Vanderplas, A. Passos, D. Cournapeau, M. Brucher, M. Perrot, and E. Duchesnay 2011 · 2011
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A kernel two-sample test
Gretton, A., K. M. Borgwardt, M. J. Rasch, B. Schölkopf, and A. Smola 2012 · 2012
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A kernel two-sample test
Gretton, A., K. M. Borgwardt, M. J. Rasch, B. Schölkopf, and A. Smola 2012 · 2012
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The arcade learning environment: An evaluation platform for general agents
Bellemare, M. G., Y. Naddaf, J. Veness, and M. Bowling 2013 · 2013
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Derivative-free optimization: a review of algorithms and comparison of software implementations
Rios, L. M. and N. V. Sahinidis 2013 · 2013
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High-dimensional density ratio estimation with extensions to approximate likelihood computation
Izbicki, R., A. Lee, and C. Schafer 2014 · 2014
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Gps-abc: Gaussian process surrogate approximate bayesian computation
Meeds, E. and M. Welling 2014 · 2014
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Approximate bayesian computation and bayes’ linear analysis: toward high-dimensional abc
Nott, D. J., Y. Fan, L. Marshall, and S. Sisson 2014 · 2014
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A note on approximating abc-mcmc using flexible classifiers
Pham, K. C., D. J. Nott, and S. Chaudhuri 2014 · 2014
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Semi-automatic selection of summary statistics for abc model choice
Prangle, D., P. Fearnhead, M. P. Cox, P. J. Biggs, and N. P. French 2014 · 2014
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Accelerating abc methods using gaussian processes
Wilkinson, R. D. 2014 · 2014
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High-dimensional density ratio estimation with extensions to approximate likelihood computation
Izbicki, R., A. Lee, and C. Schafer 2014 · 2014
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A note on approximating abc-mcmc using flexible classifiers
Pham, K. C., D. J. Nott, and S. Chaudhuri 2014 · 2014
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Approximating likelihood ratios with calibrated discriminative classifiers
Cranmer, K., J. Pavez, and G. Louppe 2015 · 2015
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On the decreasing power of kernel and distance based nonparametric hypothesis tests in high dimensions
Ramdas, A., S. J. Reddi, B. Poczos, A. Singh, and L. Wasserman 2015 · 2015
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Variational inference with normalizing flows
Rezende, D. and S. Mohamed 2015 · 2015
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Imagenet large scale visual recognition challenge
Russakovsky, O., J. Deng, H. Su, J. Krause, S. Satheesh, S. Ma, Z. Huang, A. Karpathy, A. Khosla, M. Bernstein, et al. 2015 · 2015
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Taking the human out of the loop: A review of bayesian optimization
Shahriari, B., K. Swersky, Z. Wang, R. P. Adams, and N. De Freitas 2015 · 2015
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Approximating likelihood ratios with calibrated discriminative classifiers
Cranmer, K., J. Pavez, and G. Louppe 2015 · 2015
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Cosmoabc: likelihood-free inference via population monte carlo approximate bayesian computation
Ishida, E., S. Vitenti, M. Penna-Lima, J. Cisewski, R. de Souza, A. Trindade, E. Cameron, V. Busti, C. collaboration, et al. 2015 · 2015
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Adam: A method for stochastic optimization
Kingma, D. P. and J. Ba 2015 · 2015
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On the decreasing power of kernel and distance based nonparametric hypothesis tests in high dimensions
Ramdas, A., S. J. Reddi, B. Poczos, A. Singh, and L. Wasserman 2015 · 2015
Cited alongside, same era.
A kernel test of goodness of fit
Chwialkowski, K., H. Strathmann, and A. Gretton 2016 · 2016
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Benchmarking deep reinforcement learning for continuous control
Duan, Y., X. Chen, R. Houthooft, J. Schulman, and P. Abbeel 2016 · 2016
Cited alongside, same era.
Likelihood-free inference by ratio estimation
Dutta, R., J. Corander, S. Kaski, and M. U. Gutmann 2016 · 2016
Cited alongside, same era.
Bayesian optimization for likelihood-free inference of simulator-based statistical models
pyabc: distributed, likelihood-free inference
Klinger, E., D. Rickert, and J. Hasenauer 2018 · 2018
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A guide to general-purpose abc software
Kousathanas, A., P. Duchen, and D. Wegmann 2018 · 2018
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Elfi: engine for likelihood-free inference
Lintusaari, J., H. Vuollekoski, A. Kangasrääsiö, K. Skytén, M. Järvenpää, P. Marttinen, M. U. Gutmann, A. Vehtari, J. Corander, and S. Kaski 2018 · 2018
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Accept–reject methods
Martino, L., D. Luengo, and J. Míguez 2018 · 2018
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Validating bayesian inference algorithms with simulation-based calibration
Talts, S., M. Betancourt, D. Simpson, A. Vehtari, and A. Gelman 2018 · 2018
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Etalumis: bringing probabilistic programming to scientific simulators at scale
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Gutmann, M. U. and J. Corander 2016 · 2016
Cited alongside, same era.
A kernelized stein discrepancy for goodness-of-fit tests
Liu, Q., J. Lee, and M. Jordan 2016 · 2016
Cited alongside, same era.
Fast ϵ \epsilon -free inference of simulation models with bayesian conditional density estimation
Papamakarios, G. and I. Murray 2016 · 2016
Cited alongside, same era.
A kernel test of goodness of fit
Chwialkowski, K., H. Strathmann, and A. Gretton 2016 · 2016
Cited alongside, same era.
Likelihood-free inference by ratio estimation
Dutta, R., J. Corander, S. Kaski, and M. U. Gutmann 2016 · 2016
Cited alongside, same era.
Bayesian optimization for likelihood-free inference of simulator-based statistical models
Gutmann, M. U. and J. Corander 2016 · 2016
Cited alongside, same era.
A kernelized stein discrepancy for goodness-of-fit tests
Liu, Q., J. Lee, and M. Jordan 2016 · 2016
Cited alongside, same era.
Baydin, A. G., L. Shao, W. Bhimji, L. Heinrich, L. Meadows, J. Liu, A. Munk, S. Naderiparizi, B. Gram-Hansen, G. Louppe, et al. 2019 · 2019
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Pyro: Deep universal probabilistic programming
Bingham, E., J. P. Chen, M. Jankowiak, F. Obermeyer, N. Pradhan, T. Karaletsos, R. Singh, P. Szerlip, P. Horsfall, and N. D. Goodman 2019 · 2019
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Neural spline flows
Durkan, C., A. Bekasov, I. Murray, and G. Papamakarios 2019 · 2019
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A systematic comparison of bayesian deep learning robustness in diabetic retinopathy tasks
Filos, A., S. Farquhar, A. N. Gomez, T. G. Rudner, Z. Kenton, L. Smith, M. Alizadeh, A. de Kroon, and Y. Gal 2019 · 2019
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Automatic posterior transformation for likelihood-free inference
Greenberg, D., M. Nonnenmacher, and J. Macke 2019 · 2019
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Efficient acquisition rules for model-based approximate bayesian computation
Järvenpää, M., M. U. Gutmann, A. Pleska, A. Vehtari, P. Marttinen, et al. 2019 · 2019
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Likelihood-free inference with emulator networks
Lueckmann, J.-M., G. Bassetto, T. Karaletsos, and J. H. Macke 2019 · 2019
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Pytorch: An imperative style, high-performance deep learning library
Paszke, A., S. Gross, F. Massa, A. Lerer, J. Bradbury, G. Chanan, T. Killeen, Z. Lin, N. Gimelshein, L. Antiga, A. Desmaison, A. Kopf, E. Yang, Z. DeVito, M. Raison, A. Tejani, S. Chilamkurthy, B. Steiner, L. Fang, J. Bai, and S. Chintala 2019 · 2019
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Abc random forests for bayesian parameter inference
Raynal, L., J.-M. Marin, P. Pudlo, M. Ribatet, C. P. Robert, and A. Estoup 2019 · 2019
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Hydra - a framework for elegantly configuring complex applications
Yadan, O. 2019 · 2019
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pydelfi: Density estimation likelihood-free inference
Alsing, J. 2019 · 2019
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Pyro: Deep universal probabilistic programming
Bingham, E., J. P. Chen, M. Jankowiak, F. Obermeyer, N. Pradhan, T. Karaletsos, R. Singh, P. Szerlip, P. Horsfall, and N. D. Goodman 2019 · 2019
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Neural spline flows
Durkan, C., A. Bekasov, I. Murray, and G. Papamakarios 2019 · 2019
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Automatic posterior transformation for likelihood-free inference
Greenberg, D., M. Nonnenmacher, and J. Macke 2019 · 2019
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Hypothesis
Hermans, J. 2019 · 2019
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Likelihood-free inference with emulator networks
Lueckmann, J.-M., G. Bassetto, T. Karaletsos, and J. H. Macke 2019 · 2019
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Pytorch: An imperative style, high-performance deep learning library
Paszke, A., S. Gross, F. Massa, A. Lerer, J. Bradbury, G. Chanan, T. Killeen, Z. Lin, N. Gimelshein, L. Antiga, A. Desmaison, A. Kopf, E. Yang, Z. DeVito, M. Raison, A. Tejani, S. Chilamkurthy, B. Steiner, L. Fang, J. Bai, and S. Chintala 2019 · 2019
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Abc random forests for bayesian parameter inference
Raynal, L., J.-M. Marin, P. Pudlo, M. Ribatet, C. P. Robert, and A. Estoup 2019 · 2019
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Likelihood-free inference with deep gaussian processes
Aushev, A., H. Pesonen, M. Heinonen, J. Corander, and S. Kaski 2020 · 2020
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Mining gold from implicit models to improve likelihood-free inference
Brehmer, J., G. Louppe, J. Pavez, and K. Cranmer 2020 · 2020
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Component-wise approximate bayesian computation via gibbs-like steps
Clarté, G., C. P. Robert, R. J. Ryder, and J. Stoehr 2020 · 2020
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Bringing abc inference to the machine learning realm: Abcranger, an optimized random forests library for abc
Collin, F.-D., A. Estoup, J.-M. Marin, and L. Raynal 2020 · 2020
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The frontier of simulation-based inference
Cranmer, K., J. Brehmer, and G. Louppe 2020 · 2020
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Validation of approximate likelihood and emulator models for computationally intensive simulations
Dalmasso, N., A. B. Lee, R. Izbicki, T. Pospisil, and C.-A. Lin 2020 · 2020
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On contrastive learning for likelihood-free inference
Durkan, C., I. Murray, and G. Papamakarios 2020 · 2020
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Training deep neural density estimators to identify mechanistic models of neural dynamics
Gonçalves, P. J., J.-M. Lueckmann, M. Deistler, M. Nonnenmacher, K. Öcal, G. Bassetto, C. Chintaluri, W. F. Podlaski, S. A. Haddad, T. P. Vogels, D. S. Greenberg, and J. H. Macke 2020 · 2020
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Array programming with numpy
Harris, C. R., K. J. Millman, S. J. van der Walt, R. Gommers, P. Virtanen, D. Cournapeau, E. Wieser, J. Taylor, S. Berg, N. J. Smith, et al. 2020 · 2020
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Likelihood-free mcmc with approximate likelihood ratios
Hermans, J., V. Begy, and G. Louppe 2020 · 2020
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Parallel gaussian process surrogate bayesian inference with noisy likelihood evaluations
Järvenpää, M., M. U. Gutmann, A. Vehtari, P. Marttinen, et al. 2020 · 2020
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Learning deep kernels for non-parametric two-sample tests
Liu, F., W. Xu, J. Lu, G. Zhang, A. Gretton, and D. J. Sutherland 2020 · 2020
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pandas-dev/pandas: Pandas
pandas development team, T. 2020 · 2020
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Bayesflow: Learning complex stochastic models with invertible neural networks
Radev, S. T., U. K. Mertens, A. Voss, L. Ardizzone, and U. Köthe 2020 · 2020
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Likelihood-free approximate gibbs sampling
Rodrigues, G., D. J. Nott, and S. Sisson 2020 · 2020
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Adaptive approximate bayesian computation tolerance selection
Simola, U., J. Cisewski-Kehe, M. U. Gutmann, J. Corander, et al. 2020 · 2020
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sbi: A toolkit for simulation-based inference
Tejero-Cantero, A., J. Boelts, M. Deistler, J.-M. Lueckmann, C. Durkan, P. J. Gonçalves, D. S. Greenberg, and J. H. Macke 2020 · 2020
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Likelihood-free inference by ratio estimation
Thomas, O., R. Dutta, J. Corander, S. Kaski, and M. U. Gutmann 2020 · 2020
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How good is the bayes posterior in deep neural networks really?
Wenzel, F., K. Roth, B. S. Veeling, J. Świątkowski, L. Tran, S. Mandt, J. Snoek, T. Salimans, R. Jenatton, and S. Nowozin 2020 · 2020
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Bringing abc inference to the machine learning realm: Abcranger, an optimized random forests library for abc
Collin, F.-D., A. Estoup, J.-M. Marin, and L. Raynal 2020 · 2020
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Validation of approximate likelihood and emulator models for computationally intensive simulations
Dalmasso, N., A. B. Lee, R. Izbicki, T. Pospisil, and C.-A. Lin 2020 · 2020
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On contrastive learning for likelihood-free inference
Durkan, C., I. Murray, and G. Papamakarios 2020 · 2020
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Training deep neural density estimators to identify mechanistic models of neural dynamics
Gonçalves, P. J., J.-M. Lueckmann, M. Deistler, M. Nonnenmacher, K. Öcal, G. Bassetto, C. Chintaluri, W. F. Podlaski, S. A. Haddad, T. P. Vogels, D. S. Greenberg, and J. H. Macke 2020 · 2020
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Likelihood-free mcmc with approximate likelihood ratios
Hermans, J., V. Begy, and G. Louppe 2020 · 2020
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Learning deep kernels for non-parametric two-sample tests
Liu, F., W. Xu, J. Lu, G. Zhang, A. Gretton, and D. J. Sutherland 2020 · 2020
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Multivariate normal transition
pyABC API Documentation 2020 · 2020
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Adaptive approximate bayesian computation tolerance selection
Simola, U., J. Cisewski-Kehe, M. U. Gutmann, J. Corander, et al. 2020 · 2020
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sbi: A toolkit for simulation-based inference
Tejero-Cantero, A., J. Boelts, M. Deistler, J.-M. Lueckmann, C. Durkan, P. J. Gonçalves, D. S. Greenberg, and J. H. Macke 2020 · 2020
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Likelihood-free inference by ratio estimation
Thomas, O., R. Dutta, J. Corander, S. Kaski, and M. U. Gutmann 2020 · 2020
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Neural approximate sufficient statistics for implicit models
Chen, Y., D. Zhang, M. Gutmann, A. Courville, and Z. Zhu 2021 · 2021
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Approximate bayesian computation in population genetics
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Approximate bayesian computation in population genetics
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