cCorrGAN: conditional correlation GAN for learning empirical conditional distributions in the elliptope
From MaRDI portal
Publication:2117912
DOI10.1007/978-3-030-80209-7_66OpenAlexW3186539546MaRDI QIDQ2117912
Gautier Marti, Frank Nielsen, Victor Goubet
Publication date: 22 March 2022
Full work available at URL: https://arxiv.org/abs/2107.10606
Monte Carlo simulationscorrelation matricesempirical distributionsquantitative financegenerative adversarial networkselliptope geometry
Learning and adaptive systems in artificial intelligence (68T05) Random matrices (algebraic aspects) (15B52)
Related Items (2)
Theoretically and Computationally Convenient Geometries on Full-Rank Correlation Matrices ⋮ cCorrGAN
Uses Software
Cites Work
- Computing the nearest correlation matrix--a problem from finance
- Generating random correlation matrices based on vines and extended onion method
- Numerically stable generation of correlation matrices and their factors
- Geometry-aware principal component analysis for symmetric positive definite matrices
- Matrix Information Geometry
- Behavior of the NORTA method for correlated random vector generation as the dimension increases
- Clustering in Hilbert’s Projective Geometry: The Case Studies of the Probability Simplex and the Elliptope of Correlation Matrices
- Simulating realistic correlation matrices for financial applications: correlation matrices with the Perron–Frobenius property
- Quant GANs: deep generation of financial time series
- A Differential Geometric Approach to the Geometric Mean of Symmetric Positive-Definite Matrices
This page was built for publication: cCorrGAN: conditional correlation GAN for learning empirical conditional distributions in the elliptope
