Validation of Approximate Likelihood and Emulator Models for Computationally Intensive Simulations
暂无分享,去创建一个
Rafael Izbicki | Ann B. Lee | Taylor Pospisil | Niccolo Dalmasso | Ilmun Kim | Chieh-An Lin | Rafael Izbicki | Chieh-An Lin | Niccolò Dalmasso | I. Kim | T. Pospisil | Ilmun Kim
[1] A. B. Lee,et al. Local two-sample testing: a new tool for analysing high-dimensional astronomical data , 2017, 1707.04592.
[2] D. J. Nott,et al. Approximate Bayesian computation via regression density estimation , 2012, 1212.1479.
[3] Iain Murray,et al. Masked Autoregressive Flow for Density Estimation , 2017, NIPS.
[4] P. Schneider,et al. KiDS-450: cosmological parameter constraints from tomographic weak gravitational lensing , 2016, 1606.05338.
[5] Tom Charnock,et al. Fast likelihood-free cosmology with neural density estimators and active learning , 2019, Monthly Notices of the Royal Astronomical Society.
[6] Richard Wilkinson,et al. Accelerating ABC methods using Gaussian processes , 2014, AISTATS.
[7] M. Wand,et al. Multivariate plug-in bandwidth selection , 1994 .
[8] Chieh-An Lin,et al. A New Model to Predict Weak Lensing Peak Counts , 2014, Proceedings of the International Astronomical Union.
[9] Ludovic van Waerbeke,et al. Simulations of weak gravitational lensing – II. Including finite support effects in cosmic shear covariance matrices , 2014, 1406.0543.
[10] L. Baringhaus,et al. On a new multivariate two-sample test , 2004 .
[11] Shakir Mohamed,et al. Learning in Implicit Generative Models , 2016, ArXiv.
[12] S. Wood. Statistical inference for noisy nonlinear ecological dynamic systems , 2010, Nature.
[13] Jakob H. Macke,et al. Flexible statistical inference for mechanistic models of neural dynamics , 2017, NIPS.
[14] K.,et al. The Community Earth System Model (CESM) large ensemble project: a community resource for studying climate change in the presence of internal climate variability , 2015 .
[15] Christopher C. Drovandi,et al. Variational Bayes with synthetic likelihood , 2016, Statistics and Computing.
[16] Iain Murray,et al. Sequential Neural Likelihood: Fast Likelihood-free Inference with Autoregressive Flows , 2018, AISTATS.
[17] Frank D. Wood,et al. Inference Compilation and Universal Probabilistic Programming , 2016, AISTATS.
[18] C. B. D'Andrea,et al. Cosmology constraints from shear peak statistics in Dark Energy Survey Science Verification data , 2016, 1603.05040.
[19] Douglas W. Nychka,et al. A new ensemble-based consistency test for the Community Earth System Model , 2015 .
[20] Ann B. Lee,et al. ABC–CDE: Toward Approximate Bayesian Computation With Complex High-Dimensional Data and Limited Simulations , 2018, Journal of Computational and Graphical Statistics.
[21] C. J. Conselice,et al. New image statistics for detecting disturbed galaxy morphologies at high redshift , 2013, 1306.1238.
[22] Xin Tong,et al. A plug-in approach to neyman-pearson classification , 2013, J. Mach. Learn. Res..
[23] Bernhard Schölkopf,et al. Informative Features for Model Comparison , 2018, NeurIPS.
[24] Takafumi Kanamori,et al. $f$ -Divergence Estimation and Two-Sample Homogeneity Test Under Semiparametric Density-Ratio Models , 2010, IEEE Transactions on Information Theory.
[25] Aki Vehtari,et al. Validating Bayesian Inference Algorithms with Simulation-Based Calibration , 2018, 1804.06788.
[26] Ann B. Lee,et al. Geometric diffusions as a tool for harmonic analysis and structure definition of data: diffusion maps. , 2005, Proceedings of the National Academy of Sciences of the United States of America.
[27] Gilles Louppe,et al. Mining gold from implicit models to improve likelihood-free inference , 2018, Proceedings of the National Academy of Sciences.
[28] Olivier Thas,et al. Comparing Distributions , 2009 .
[29] Arthur Gretton,et al. Interpretable Distribution Features with Maximum Testing Power , 2016, NIPS.
[30] S. Ravindranath,et al. CANDELS: THE COSMIC ASSEMBLY NEAR-INFRARED DEEP EXTRAGALACTIC LEGACY SURVEY—THE HUBBLE SPACE TELESCOPE OBSERVATIONS, IMAGING DATA PRODUCTS, AND MOSAICS , 2011, 1105.3753.
[31] Masanori Sato,et al. SIMULATIONS OF WIDE-FIELD WEAK-LENSING SURVEYS. II. COVARIANCE MATRIX OF REAL-SPACE CORRELATION FUNCTIONS , 2010, 1009.2558.
[32] Jakob H. Macke,et al. Likelihood-free inference with emulator networks , 2018, AABI.
[33] Yoshua Bengio,et al. NICE: Non-linear Independent Components Estimation , 2014, ICLR.
[34] Ritabrata Dutta,et al. Likelihood-free inference via classification , 2014, Stat. Comput..
[35] W. Collins,et al. The Community Earth System Model: A Framework for Collaborative Research , 2013 .
[36] David T. Frazier,et al. Bayesian Synthetic Likelihood , 2017, 2305.05120.
[37] Barnabás Póczos,et al. Enabling Dark Energy Science with Deep Generative Models of Galaxy Images , 2016, AAAI.
[38] Yanan Fan,et al. Handbook of Approximate Bayesian Computation , 2018 .
[39] Hugo Larochelle,et al. Neural Autoregressive Distribution Estimation , 2016, J. Mach. Learn. Res..
[40] Gilles Louppe,et al. Approximating Likelihood Ratios with Calibrated Discriminative Classifiers , 2015, 1506.02169.
[41] Benjamin Dan Wandelt,et al. Massive optimal data compression and density estimation for scalable, likelihood-free inference in cosmology , 2018, 1801.01497.
[42] Aki Vehtari,et al. Gaussian process modelling in approximate Bayesian computation to estimate horizontal gene transfer in bacteria , 2016, The Annals of Applied Statistics.
[43] Christopher K. I. Williams,et al. Gaussian Processes for Machine Learning (Adaptive Computation and Machine Learning) , 2005 .
[44] Max Welling,et al. GPS-ABC: Gaussian Process Surrogate Approximate Bayesian Computation , 2014, UAI.
[45] J. P. Dietrich,et al. Cosmology with the shear-peak statistics , 2009, 0906.3512.
[46] Eric Nalisnick,et al. Normalizing Flows for Probabilistic Modeling and Inference , 2019, J. Mach. Learn. Res..
[47] Arthur Gretton,et al. Fast Two-Sample Testing with Analytic Representations of Probability Measures , 2015, NIPS.
[48] Jean-Michel Marin,et al. Approximate Bayesian computational methods , 2011, Statistics and Computing.
[49] D. Balding,et al. Approximate Bayesian computation in population genetics. , 2002, Genetics.
[50] Alex Graves,et al. Conditional Image Generation with PixelCNN Decoders , 2016, NIPS.
[51] Michael U. Gutmann,et al. Bayesian Optimization for Likelihood-Free Inference of Simulator-Based Statistical Models , 2015, J. Mach. Learn. Res..
[52] Z. Bai,et al. A review of 20 years of naive tests of significance for high-dimensional mean vectors and covariance matrices , 2016, 1603.01003.
[53] Michael U. Gutmann,et al. Dynamic Likelihood-free Inference via Ratio Estimation (DIRE) , 2018, ArXiv.
[54] Larry A. Wasserman,et al. Classification Accuracy as a Proxy for Two Sample Testing , 2016, The Annals of Statistics.
[55] G. Székely,et al. TESTING FOR EQUAL DISTRIBUTIONS IN HIGH DIMENSION , 2004 .
[56] Iain Murray,et al. Fast $\epsilon$-free Inference of Simulation Models with Bayesian Conditional Density Estimation , 2016, 1605.06376.
[57] Kenji Fukumizu,et al. A Linear-Time Kernel Goodness-of-Fit Test , 2017, NIPS.
[58] Gilles Louppe,et al. Constraining Effective Field Theories with Machine Learning. , 2018, Physical review letters.
[59] Gilles Louppe,et al. Likelihood-free inference with an improved cross-entropy estimator , 2018, ArXiv.
[60] Max Welling,et al. Improved Variational Inference with Inverse Autoregressive Flow , 2016, NIPS 2016.
[61] S. Sisson,et al. Diagnostic tools for approximate Bayesian computation using the coverage property , 2013, 1301.3166.
[62] Takafumi Kanamori,et al. Least-squares two-sample test , 2011, Neural Networks.
[63] Rafael Izbicki,et al. High-Dimensional Density Ratio Estimation with Extensions to Approximate Likelihood Computation , 2014, AISTATS.
[64] J. Friedman. Greedy function approximation: A gradient boosting machine. , 2001 .
[65] Prafulla Dhariwal,et al. Glow: Generative Flow with Invertible 1x1 Convolutions , 2018, NeurIPS.
[66] Samy Bengio,et al. Density estimation using Real NVP , 2016, ICLR.
[67] Matthias Bethge,et al. Generative Image Modeling Using Spatial LSTMs , 2015, NIPS.
[68] Jukka Corander,et al. Likelihood-Free Inference by Ratio Estimation , 2016, Bayesian Analysis.
[69] Ann B. Lee,et al. Global and local two-sample tests via regression , 2018, Electronic Journal of Statistics.
[70] Hugo Larochelle,et al. MADE: Masked Autoencoder for Distribution Estimation , 2015, ICML.
[71] Heiga Zen,et al. WaveNet: A Generative Model for Raw Audio , 2016, SSW.
[72] Yoshua Bengio,et al. Generative Adversarial Nets , 2014, NIPS.
[73] Donald B. Rubin,et al. Validation of Software for Bayesian Models Using Posterior Quantiles , 2006 .
[74] Shakir Mohamed,et al. Variational Inference with Normalizing Flows , 2015, ICML.
[75] Deborah Bard,et al. CosmoGAN: creating high-fidelity weak lensing convergence maps using Generative Adversarial Networks , 2017, Computational Astrophysics and Cosmology.
[76] Bernhard Schölkopf,et al. A Kernel Two-Sample Test , 2012, J. Mach. Learn. Res..
[77] Hugo Larochelle,et al. A Deep and Tractable Density Estimator , 2013, ICML.
[78] Daniel J. Hsu,et al. Non-Gaussian information from weak lensing data via deep learning , 2018, ArXiv.
[79] C. B. D'Andrea,et al. Cosmology from cosmic shear with Dark Energy Survey science verification data , 2015, 1507.05552.
[80] Scott A. Sisson,et al. Does Amazonian deforestation cause global effects; can we be sure? , 2016 .
[81] E. Lehmann. Testing Statistical Hypotheses , 1960 .
[82] J. Friedman. On Multivariate Goodness-of-Fit and Two-Sample Testing , 2004 .
[83] Koray Kavukcuoglu,et al. Pixel Recurrent Neural Networks , 2016, ICML.
[84] David Duvenaud,et al. Neural Ordinary Differential Equations , 2018, NeurIPS.
[85] David S. Greenberg,et al. Automatic Posterior Transformation for Likelihood-Free Inference , 2019, ICML.
[86] David Lopez-Paz,et al. Revisiting Classifier Two-Sample Tests , 2016, ICLR.