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We introduce a framework for Bayesian experimental design (BED) with implicit models, where the data-generating distribution is intractable but sampling from it is still possible.
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“The IM Algorithm: A Variational Approach to Information Maximization.”
Barber, D. and Agakov, F. (2003) · 2003
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“Estimating Expected Information Gains for Experimental Designs with Application to the Random Fatigue-Limit Model.”
Ryan, K. J. (2003) · 2003
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“Asymptotic Theory of Information-Theoretic Experimental Design.”
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“CLUB: A Contrastive Log-ratio Upper Bound of Mutual Information.”
Cheng, P., Hao, W., Dai, S., Liu, J., Gan, Z., and Carin, L. (2020) · 2006
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“Statistical Inference in a Stochastic Epidemic SEIR Model with Control Intervention: Ebola as a Case Study.”
Lekone, P. E. and Finkenstädt, B. F. (2006) · 2006
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“Construction of Equivalent Stochastic Differential Equation Models.”
Allen, E. J., Allen, L. J. S., Arciniega, A., and Greenwood, P. E. (2008) · 2008
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Mathematical Epidemiology
Allen, L. J. S. (2008) · 2008
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“Estimating Divergence Functionals and the Likelihood Ratio by Convex Risk Minimization.”
Nguyen, X., Wainwright, M. J., and Jordan, M. I. (2010) · 2010
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“Noise-contrastive estimation of unnormalized statistical models, with applications to natural image statistics.”
Gutmann, M. and Hyvärinen, A. (2012) · 2012
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“Likelihood-Free Inference in Cosmology: Potential for the Estimation of Luminosity Functions.”
Schafer, C. M. and Freeman, P. E. (2012) · 2012
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“Bayesian Experimental Design for Models with Intractable Likelihoods.”
Drovandi, C. C. and Pettitt, A. N. (2013) · 2013
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“Maximizing the Information Content of Experiments in Systems Biology.”
Liepe, J., Filippi, S., Komorowski, M., and Stumpf, M. P. H. (2013) · 2013
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“TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems.”
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“An induced natural selection heuristic for finding optimal Bayesian experimental designs.”
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Sisson, S., Fan, Y., and Beaumont, M. (2018) · 2018
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“Representation Learning with Contrastive Predictive Coding.”
van den Oord, A., Li, Y., and Vinyals, O. (2018) · 2018
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“Adaptive Gaussian Copula ABC.”
Chen, Y. and Gutmann, M. (2019) · 2019
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“Variational Bayesian Optimal Experimental Design.”
Foster, A., Jankowiak, M., Bingham, E., Horsfall, P., Teh, Y. W., Rainforth, T., and Goodman, N. (2019) · 2019
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“Learning deep representations by mutual information estimation and maximization.”
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“Bayesian optimization for likelihood-free inference of simulator-based statistical models.”
Gutmann, M. and Corander, J. (2016) · 2016
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“Likelihood-free simulation-based optimal design with an application to spatial extremes.”
Hainy, M., Müller, W. G., and Wagner, H. (2016) · 2016
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“F-GAN: Training Generative Neural Samplers Using Variational Divergence Minimization.”
Nowozin, S., Cseke, B., and Tomioka, R. (2016) · 2016
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“A Review of Modern Computational Algorithms for Bayesian Optimal Design.”
Ryan, E. G., Drovandi, C. C., McGree, J. M., and Pettitt, A. N. (2016) · 2016
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“A primer on stochastic epidemic models: Formulation, numerical simulation, and analysis.”
Allen, L. J. (2017) · 2017
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“Fundamentals and Recent Developments in Approximate Bayesian Computation.”
Lintusaari, J., Gutmann, M., 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., Gonçalves, P. J., Bassetto, G., Öcal, K., Nonnenmacher, M., and Macke, J. H. (2017) · 2017
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Hjelm, R. D., Fedorov, A., Lavoie-Marchildon, S., Grewal, K., Bachman, P., Trischler, A., and Bengio, Y. (2019) · 2019
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“Efficient Bayesian Experimental Design for Implicit Models.”
Kleinegesse, S. and Gutmann, M. U. (2019) · 2019
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“Sequential Neural Likelihood: Fast Likelihood-free Inference with Autoregressive Flows.”
Papamakarios, G., Sterratt, D., and Murray, I. (2019) · 2019
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“PyTorch: An Imperative Style, High-Performance Deep Learning Library.”
Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., and Chintala, S. (2019) · 2019
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“The frontier of simulation-based inference.”
Cranmer, K., Brehmer, J., and Louppe, G. (2020) · 2020
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“Scalable gradients for stochastic differential equations.”
Li, X., Wong, T.-K. L., Chen, R. T. Q., and Duvenaud, D. (2020) · 2020
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“Monte Carlo Gradient Estimation in Machine Learning.”
Mohamed, S., Rosca, M., Figurnov, M., and Mnih, A. (2020) · 2020
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“Bayesian Design of Experiments for Intractable Likelihood Models Using Coupled Auxiliary Models and Multivariate Emulation.”
Overstall, A. and McGree, J. (2020) · 2020
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“Understanding the Limitations of Variational Mutual Information Estimators.”
Song, J. and Ermon, S. (2020) · 2020
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“Likelihood-Free Inference by Ratio Estimation.”
Thomas, O., Dutta, R., Corander, J., Kaski, S., and Gutmann, M. U. (2020) · 2020
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“Neural Approximate Sufficient Statistics for Implicit Models.”
Chen, Y., Zhang, D., Gutmann, M. U., Courville, A., and Zhu, Z. (2021) · 2021
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“Deep Adaptive Design: Amortizing Sequential Bayesian Experimental Design.”
Foster, A., Ivanova, D. R., Malik, I., and Rainforth, T. (2021) · 2021
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Bayesian Analysis
Järvenpää, M., Gutmann, M. U., Vehtari, A., and Marttinen, P. (2021) · 2021
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“A Scalable Gradient Free Method for Bayesian Experimental Design with Implicit Models.”
Zhang, J., Bi, S., and Zhang, G. (2021) · 2021
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