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We introduce Sequential Neural Posterior Score Estimation (SNPSE), a score-based method for Bayesian inference in simulator-based models.
Analytical note on certain rhythmic relations in organic systems
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Comment on ‘The calculation of posterior distributions by data augmentation’ by Tanner M. and Wong W.H
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Bayesian statistics without tears: a sampling–resampling perspective
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Indirect inference
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Bayesian data analysis
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Nonparametric input estimation in physiological systems: Problems, methods, and case studies
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Inferring coalescence times from DNA sequence data
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Inferences for case-control and semiparametric two-sample density ratio models
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Population growth of human Y chromosomes: a study of Y chromosome microsatellites
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Bartholomew-Biggs, M., Brown, S., Christianson, B., and Dixon, L · 2000
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Markov chain Monte Carlo without likelihoods
Marjoram, P., Molitor, J., Plagnol, V., and Tavaré, S · 2003
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Alternative to hand-tuning conductance-based models: construction and analysis of databases of model neurons
Prinz, A. A., Billimoria, C. P., and Marder, E · 2003
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Semiparametric density estimation under a two-sample density ratio model
Cheng, K. F. and Chu, C. K · 2004
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Similar network activity from disparate circuit parameters
Prinz, A. A., Bucher, D., and Marder, E · 2004
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Estimation of non-normalized statistical models by score matching
Hyvärinen, A · 2005
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Pattern Recognition and Machine Learning
Bishop, C · 2006
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Discriminative learning for differing training and test distributions
Bickel, S., Brückner, M., and Scheffer, T · 2007
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Importance sampling via the estimated sampler
Henmi, M., Yoshida, R., and Eguchi, S · 2007
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Using likelihood-free inference to compare evolutionary dynamics of the protein networks of H. pylori and P. falciparum
Ratmann, O., Jørgensen, O., Hinkley, T., Stumpf, M., Richardson, S., and Wiuf, C · 2007
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Sequential Monte Carlo without likelihoods
Sisson, S. A., Fan, Y., and Tanaka, M. M · 2007
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Direct importance estimation for covariate shift adaptation
Sugiyama, M., Suzuki, T., Nakajima, S., Kashima, H., von Bünau, P., and Kawanabe, M · 2008
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Adaptive approximate Bayesian computation
Beaumont, M. A., Cornuet, J.-M., Marin, J.-M., and Robert, C. P · 2009
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Covariate shift by kernel mean matching
Gretton, A., Smola, A., Huang, J., Schmittfull, M., Borgwardt, K., and Scholkopf, B · 2009
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A least squares approach to direct importance estimation
Kanamori, T., Hido, S., and Sugiyama, M · 2009
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Direct density ratio estimation for large-scale covariate shift adaptation
Tsuboi, Y., Kashima, H., Hido, S., Bickel, S., and Sugiyama, M · 2009
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Direct importance estimation with Gaussian mixture models
Yamada, M. and Sugiyama, M · 2009
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Approximate Bayesian computation in evolution and ecology
Beaumont, M. A · 2010
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Statistical inference for noisy nonlinear ecological dynamic systems
Wood, S. N · 2010
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Handbook of Markov Chain Monte Carlo
Brooks, S., Gelman, A., Jones, G., and Meng, X.-L · 2011
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Principles of Computational Modelling in Neuroscience
Sterratt, D., Graham, B., Gillies, A., and Willshaw, D · 2011
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A connection between score matching and denoising autoencoders
Vincent, P · 2011
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Density Ratio Estimation in Machine Learning
Sugiyama, M., Suzuki, T., and Kanamori, T · 2012
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Generative adversarial nets
Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., and Bengio, Y · 2014
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High-dimensional density ratio estimation with extensions to approximate likelihood computation
Izbicki, R., Lee, A. B., and Schafer, C. M · 2014
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A note on approximating ABC-MCMC using flexible classifiers
Pham, K. C., Nott, D. J., and Chaudhuri, S · 2014
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Sequential Monte Carlo with adaptive weights for approximate Bayesian computation
Bonassi, F. V. and West, M · 2015
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Adam: a method for stochastic optimisation
Kingma, D. P. and Ba, J · 2015
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Deep unsupervised learning using nonequilibrium thermodynamics
Sohl-Dickstein, J., Weiss, E. A., Maheswaranathan, N., and Ganguli, S · 2015
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Approximating likelihood ratios with calibrated discriminative classifiers
Cranmer, K., Pavez, J., and Louppe, G · 2016
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Integral approximation by kernel smoothing
Delyon, B. and Portier, F · 2016
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A kernelized Stein discrepancy for goodness-of-fit tests
Liu, Q., Lee, J. D., and Jordan, M · 2016
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Fast ϵ \epsilon -free inference of simulation models with Bayesian conditional density estimation
Papamakarios, G. and Murray, I · 2016
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Improved techniques for training GANs
Salimans, T., Goodfellow, I., Zaremba, W., Cheung, V., Radford, A., Chen, X., and Chen, X · 2016
Sliced score matching: a scalable approach to density and score estimation
Song, Y., Garg, S., Shi, J., and Ermon, S · 2020
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Conditional image generation with score-based diffusion models
Batzolis, G., Stanczuk, J., Schönlieb, C.-B., and Etmann, C · 2021
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Simulation-based inference in particle physics
Brehmer, J · 2021
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WaveGrad: estimating gradients for waveform generation
Chen, N., Zhang, Y., Zen, H., Weiss, R. J., Norouzi, M., and Chan, W · 2021
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Diffusion models beat GANs on image synthesis
Dhariwal, P. and Nichol, A · 2021
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Recordings from the c. borealis stomatogastric nervous system at different temperatures in the decentralized condition, 2021
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Variational inference: a review for statisticians
Blei, D. M., Kucukelbir, A., and McAuliffe, J. D · 2017
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Frequency-dependent selection in vaccine-associated pneumococcal population dynamics
Corander, J., Fraser, C., Gutmann, M. U., Arnold, B., Hanage, W. P., Bentley, S. D., Lipsitch, M., and Croucher, N. J · 2017
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Density estimation using Real NVP
Dinh, L., Sohl-Dickstein, J., and Bengio, S · 2017
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Black-box importance sampling
Liu, Q. and Lee, J. D · 2017
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Revisiting classifier two-sample tests
Lopez-Paz, D. and Oquab, M · 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
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Haddad, S. A. and Marder, E · 2021
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DiffWave: a versatile diffusion model for audio synthesis
Kong, Z., Ping, W., Huang, J., Zhao, K., and Catanzaro, B · 2021
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Benchmarking simulation-based inference
Lueckmann, J.-M., Boelts, J., Greenberg, D., Goncalves, P., and Macke, J · 2021
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Truncated marginal neural ratio estimation
Miller, B. K., Cole, A., Forré, P., Louppe, G., and Weniger, C · 2021
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Symbolic music generation with diffusion models
Mittal, G., Engel, J., Hawthorne, C. G.-M., and Simon, I · 2021
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Normalizing flows for probabilistic modeling and inference
Papamakarios, G., Nalisnick, E., Rezende, D. J., Mohamed, S., and Lakshminarayanan, B · 2021
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Grad-TTS: a diffusion probabilistic model for text-to-speech
Popov, V., Vovk, I., Gogoryan, V., Sadekova, T., and Kudinov, M · 2021
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Should EBMs model the energy or the score?
Salimans, T. and Ho, J · 2021
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Variational likelihood-free gradient descent
Simons, J., Liu, S., and Beaumont, M · 2021
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Score-based generative modeling through stochastic differential equations
Song, Y., Sohl-Dickstein, J., Kingma, D., Kumar, A., Ermon, S., and B. Poole · 2021
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CSDI: conditional score-based diffusion models for probabilistic time series imputation
Tashiro, Y., Song, J., Song, Y., and Ermon, S · 2021
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Sequential neural posterior and likelihood approximation
Wiqvist, S., Frellsen, J., and Picchini, U · 2021
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Denoising likelihood score matching for conditional score-based data generation
Chao, C.-H., Sun, W.-F., Cheng, B.-W., Lo, Y.-C., Chang, C.-C., Liu, Y.-L., Chang, Y.-L., Chen, C.-P., and Lee, C.-Y · 2022
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Score-based diffusion models for accelerated MRI
Chung, H. and Ye, J. C · 2022
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Truncated proposals for scalable and hassle-free simulation-based inference
Deistler, M., Goncalves, P. J., and Macke, J. H · 2022
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Bayesian inference using synthetic likelihood: asymptotics and adjustments
Frazier, D. T., Nott, D. J., Drovandi, C., and Kohn, R · 2022
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Maximum likelihood learning of unnormalized models for simulation-based inference
Glaser, P., Arbel, M., Hromadka, S., Doucet, A., and Gretton, A · 2022
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Variational methods for simulation-based inference
Glockler, M., Deistler, M., and Macke, J. H · 2022
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A trust crisis in simulation-based inference? Your posterior approximations can be unfaithful
Hermans, J., Delaunoy, A., Rozet, F., Wehenkel, A., Begy, V., and Louppe, G · 2022
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Video diffusion models
Ho, J., Salimans, T., Gritsenko, A., Chan, W., Norouzi, M., and Fleet, D. J · 2022
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DPM-Solver: A Fast ODE Solver for Diffusion Probabilistic Model Sampling in Around 10 Steps
Lu, C., Zhou, Y., Bao, F., Chen, J., Li, C., and Zhu, J · 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., Gonçalves, P. J., and Macke, J. H · 2022
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Solving inverse problems in medical imaging with score-based generative models
Song, Y., Shen, L., Xing, L., and Ermon, S · 2022
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Likelihood-free inference by ratio estimation
Thomas, O., Dutta, R., Corander, J., Kaski, S., and Gutmann, M. U · 2022
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Flow matching for scalable simulation-based inference
Dax, M., Wildberger, J., Buchholz, S., Green, S. R., Macke, J. H., and Schölkopf, B · 2023
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Reduce, reuse, recycle: compositional generation with energy-based diffusion models and MCMC
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Compositional score modeling for simulation-based inference
Geffner, T., Papamakarios, G., and Mnih, A · 2023
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Flow matching for generative modelling
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Consistency models for scalable and fast simulation-based inference
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Consistency models
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Fast sampling of diffusion models with exponential integrator
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Error bounds for flow matching methods
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Unveil conditional diffusion models with classifier-free guidance: A sharp statistical theory
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