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Normalizing flows are a flexible class of probability distributions, expressed as transformations of a simple base distribution.
A simple general approach to inference about the tail of a distribution
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Dependence measures for extreme value analyses
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An Introduction to Statistical Modeling of Extreme Values
S. Coles · 2001
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Using a bootstrap method to choose the sample fraction in tail index estimation
J. Danielsson, L. de Haan, L. Peng, and C. G. de Vries · 2001
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Monte Carlo statistical methods
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An application of extreme value theory for measuring financial risk
M. Gilli and E. Këllezi · 2006
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Bootstrap and empirical likelihood methods in extremes
Y. Qi · 2008
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Error functions, Dawson’s and Fresnel integrals
N. M. Temme · 2010
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An introduction to heavy-tailed and subexponential distributions
S. Foss, D. Korshunov, and S. Zachary · 2011
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Modelling extremal events: for insurance and finance
P. Embrechts, C. Klüppelberg, and T. Mikosch · 2013
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Extended generalised Pareto models for tail estimation
I. Papastathopoulos and J. A. Tawn · 2013
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Quantile mechanics ii: changes of variables in Monte Carlo methods and GPU-optimised normal quantiles
W. T. Shaw, T. Luu, and N. Brickman · 2014
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Variational inference with normalizing flows
D. Rezende and S. Mohamed · 2015
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Modeling jointly low, moderate, and heavy rainfall intensities without a threshold selection
P. Naveau, R. Huser, P. Ribereau, and A. Hannart · 2016
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Variational inference: A review for statisticians
D. M. Blei, A. Kucukelbir, and J. D. McAuliffe · 2017
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Masked autoregressive flow for density estimation
G. Papamakarios, T. Pavlakou, and I. Murray · 2017
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Quantile function expansion using regularly varying functions
T. Fung and E. Seneta · 2018
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Neural autoregressive flows
C.-W. Huang, D. Krueger, A. Lacoste, and A. Courville · 2018
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Transformation autoregressive networks
J. Oliva, A. Dubey, M. Zaheer, B. Poczos, R. Salakhutdinov, E. Xing, and J. Schneider · 2018
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Yes, but did it work?: Evaluating variational inference
Y. Yao, A. Vehtari, D. Simpson, and A. Gelman · 2018
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Future climate risk from compound events
J. Zscheischler, S. Westra, B. van den Hurk, S. I. Seneviratne, P. J. Ward, A. J. Pitman, A. Aghakouchak, D. N. Bresch, M. Leonard, T. Wahl, and X. Zhang · 2018
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Normalizing flows for probabilistic modeling and inference
G. Papamakarios, E. Nalisnick, D. J. Rezende, S. Mohamed, and B. Lakshminarayanan · 2021
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Parametric models for distributions when interest is in extremes with an application to daily temperature
M. L. Stein · 2021
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Generating heavy-tailed synthetic data with normalizing flows
S. Amiri, E. T. Nalisnick, A. Belloum, S. Klous, and L. Gommans · 2022
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An extreme value Bayesian lasso for the conditional left and right tails
M. de Carvalho, S. Pereira, P. Pereira, and P. de Zea Bermudez · 2022
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Marginal tail-adaptive normalizing flows
M. Laszkiewicz, J. Lederer, and A. Fischer · 2022
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Fat-tailed variational inference with anisotropic tail adaptive flows
F. T. Liang, L. Hodgkinson, and M. W. Mahoney · 2022
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Variational auto-encoders with Student’s t-prior
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Neural spline flows
C. Durkan, A. Bekasov, I. Murray, and G. Papamakarios · 2019
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Sum-of-squares polynomial flow
P. Jaini, K. A. Selby, and Y. Yu · 2019
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Sequential neural likelihood: Fast likelihood-free inference with autoregressive flows
G. Papamakarios, D. Sterratt, and I. Murray · 2019
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Pytorch: An imperative style, high-performance deep learning library
A. Paszke, 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
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Tail risk of contagious diseases
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COMET flows: Towards generative modeling of multivariate extremes and tail dependence
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The fundamentals of heavy tails: Properties, emergence, and estimation
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On the maximum domain of attraction for transformations of a normal random variable
V. V. Troshin · 2022
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Flexible tails for normalising flows, with application to the modelling of financial return data
T. Hickling and D. Prangle · 2023
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Flow matching for generative modeling
Y. Lipman, R. T. Q. Chen, H. Ben-Hamu, M. Nickel, and M. Le · 2023
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Probabilistic Machine Learning: Advanced Topics
K. P. Murphy · 2023
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On the universality of volume-preserving and coupling-based normalizing flows
F. Draxler, S. Wahl, C. Schnoerr, and U. Koethe · 2024
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A heavy-tailed algebra for probabilistic programming
F. T. Liang, L. Hodgkinson, and M. W. Mahoney · 2024
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Stereographic Markov chain Monte Carlo
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Modeling of spatial extremes in environmental data science: time to move away from max-stable processes
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