Copulas in Machine Learning

By Gal Elidan. 2013

73 citations recorded by the source corpus.

Subfields: Control and Systems Engineering, Finance, Statistics and Probability

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Citations in the corpus

Causal Inference through a Witness Protection Program (2014, background): “idea, which are related to the overriding of structural equations proposed by Haavelmo (1943). Notice that if U is observed in the dataset, then we can obtain the distribution P (Y = y | do(X = x)) by simply calculating ∑ u P (Y = y |X = x, U = u)P (U = u) (Spirtes et al.”
High-Dimensional Multivariate Forecasting with Low-Rank Gaussian Copula Processes (2019, methodology): “→ , U N with uniform marginal distribution [8], i.e. is the CDF of a joint distribution of a collection of real random variables U 1 , . . . Sklar’s theorem [28] states that any joint cumulative distribution F admits a representation in terms of its univariate marginals F i and a copula function C…”
Neural Spline Flows (2019, background): “Long-standing probabilistic models such as copulas [11] and Gaussianization [4] can simply represent complex marginal distributions that would require many layers of transformations in flow-based models like RealNVP and Glow.”
Normalizing Flows for Probabilistic Modeling and Inference (2019, methodology): “Copulas (Sklar, 1959; Elidan, 2013) can be viewed as rudimentary flows, where each dimension is transformed independently using the empirically-estimated marginal cumulative distribution function.”
Multivariate spearman's ρ for aggregating ranks using copulas (2016, background): “Intuitively, for continuous random variables copulas model the dependence component of a multivariate distribution after discounting for univariate marginal effects.”
From Dependence to Causation (2016, background): “Elidan (2013) offers a monograph on the ongoing synergy between machine learning and copulas.”