暂无分享,去创建一个
[1] Jürgen Schmidhuber,et al. Learning to Forget: Continual Prediction with LSTM , 2000, Neural Computation.
[2] P. Diggle,et al. Monte Carlo Methods of Inference for Implicit Statistical Models , 1984 .
[3] Joshua B. Tenenbaum,et al. Approximate Bayesian Image Interpretation using Generative Probabilistic Graphics Programs , 2013, NIPS.
[4] Jessica T Davis,et al. The effect of travel restrictions on the spread of the 2019 novel coronavirus (COVID-19) outbreak , 2020, Science.
[5] Iain Murray,et al. Fast $\epsilon$-free Inference of Simulation Models with Bayesian Conditional Density Estimation , 2016, 1605.06376.
[6] Sergey Levine,et al. Wasserstein Dependency Measure for Representation Learning , 2019, NeurIPS.
[7] Thomas M. Cover,et al. Elements of Information Theory , 2005 .
[8] Yoshua Bengio,et al. Learning deep representations by mutual information estimation and maximization , 2018, ICLR.
[9] Iain Murray,et al. Fast $\epsilon$-free Inference of Simulation Models with Bayesian Conditional Density Estimation , 2016 .
[10] David S. Greenberg,et al. Automatic Posterior Transformation for Likelihood-Free Inference , 2019, ICML.
[11] M. Gutmann,et al. Fundamentals and Recent Developments in Approximate Bayesian Computation , 2016, Systematic biology.
[12] Michael U. Gutmann,et al. Dynamic Likelihood-free Inference via Ratio Estimation (DIRE) , 2018, ArXiv.
[13] Alberto D. Pascual-Montano,et al. A survey of dimensionality reduction techniques , 2014, ArXiv.
[14] Maria L. Rizzo,et al. Partial Distance Correlation with Methods for Dissimilarities , 2013, 1310.2926.
[15] Ohad Shamir,et al. Learning and generalization with the information bottleneck , 2008, Theor. Comput. Sci..
[16] Benjamin Dan Wandelt,et al. Massive optimal data compression and density estimation for scalable, likelihood-free inference in cosmology , 2018, 1801.01497.
[17] Sebastian Nowozin,et al. f-GAN: Training Generative Neural Samplers using Variational Divergence Minimization , 2016, NIPS.
[18] Gilles Louppe,et al. Likelihood-free MCMC with Amortized Approximate Ratio Estimators , 2019, ICML.
[19] Peter Skands,et al. A brief introduction to PYTHIA 8.1 , 2007, Comput. Phys. Commun..
[20] Iain Murray,et al. Masked Autoregressive Flow for Density Estimation , 2017, NIPS.
[21] Jukka Corander,et al. Likelihood-Free Inference by Ratio Estimation , 2016, Bayesian Analysis.
[22] Michael U. Gutmann,et al. Adaptive Gaussian Copula ABC , 2019, AISTATS.
[23] Bernhard Schölkopf,et al. A Kernel Two-Sample Test , 2012, J. Mach. Learn. Res..
[24] Yun S. Song,et al. A Likelihood-Free Inference Framework for Population Genetic Data using Exchangeable Neural Networks , 2018, bioRxiv.
[25] Iain Murray,et al. On Contrastive Learning for Likelihood-free Inference , 2020, ICML.
[26] Gilles Louppe,et al. Approximating Likelihood Ratios with Calibrated Discriminative Classifiers , 2015, 1506.02169.
[27] Ravi Bansal,et al. Risks for the Long Run: A Potential Resolution of Asset Pricing Puzzles , 2000 .
[28] Robert Leenders,et al. Hamiltonian ABC , 2015, UAI.
[29] Ryan P. Adams,et al. High-Dimensional Probability Estimation with Deep Density Models , 2013, ArXiv.
[30] S. Wood. Statistical inference for noisy nonlinear ecological dynamic systems , 2010, Nature.
[31] Jie Li,et al. A survey of dimensionality reduction techniques based on random projection , 2017, ArXiv.
[32] Paul Marjoram,et al. Markov chain Monte Carlo without likelihoods , 2003, Proceedings of the National Academy of Sciences of the United States of America.
[33] Martin J. Wainwright,et al. Estimating Divergence Functionals and the Likelihood Ratio by Convex Risk Minimization , 2008, IEEE Transactions on Information Theory.
[34] Jes Frellsen,et al. Partially Exchangeable Networks and Architectures for Learning Summary Statistics in Approximate Bayesian Computation , 2019, ICML.
[35] Iain Murray,et al. Sequential Neural Likelihood: Fast Likelihood-free Inference with Autoregressive Flows , 2018, AISTATS.
[36] Jakob H. Macke,et al. Likelihood-free inference with emulator networks , 2018, AABI.
[37] Jakob H. Macke,et al. Flexible statistical inference for mechanistic models of neural dynamics , 2017, NIPS.
[38] M. Feldman,et al. Population growth of human Y chromosomes: a study of Y chromosome microsatellites. , 1999, Molecular biology and evolution.
[39] Gilles Louppe,et al. Mining gold from implicit models to improve likelihood-free inference , 2018, Proceedings of the National Academy of Sciences.
[40] Zenglin Xu,et al. Mutual Information Gradient Estimation for Representation Learning , 2020, ICLR.
[41] Paul Fearnhead,et al. Constructing summary statistics for approximate Bayesian computation: semi‐automatic approximate Bayesian computation , 2012 .
[42] W. M. Wood-Vasey,et al. LIKELIHOOD-FREE COSMOLOGICAL INFERENCE WITH TYPE Ia SUPERNOVAE: APPROXIMATE BAYESIAN COMPUTATION FOR A COMPLETE TREATMENT OF UNCERTAINTY , 2012, 1206.2563.
[43] Aaron C. Courville,et al. MINE: Mutual Information Neural Estimation , 2018, ArXiv.
[44] Heiga Zen,et al. Parallel WaveNet: Fast High-Fidelity Speech Synthesis , 2017, ICML.
[45] A. Pettitt,et al. Approximate Bayesian computation using indirect inference , 2011 .
[46] Mark M. Tanaka,et al. Sequential Monte Carlo without likelihoods , 2007, Proceedings of the National Academy of Sciences.
[47] Koray Kavukcuoglu,et al. Pixel Recurrent Neural Networks , 2016, ICML.
[48] Bai Jiang,et al. Learning Summary Statistic for Approximate Bayesian Computation via Deep Neural Network , 2015, 1510.02175.
[49] S. Sisson,et al. A comparative review of dimension reduction methods in approximate Bayesian computation , 2012, 1202.3819.
[50] Aki Vehtari,et al. Gaussian process modelling in approximate Bayesian computation to estimate horizontal gene transfer in bacteria , 2016, The Annals of Applied Statistics.
[51] Jimmy Ba,et al. Adam: A Method for Stochastic Optimization , 2014, ICLR.