Learning Vine Copula Models For Synthetic Data Generation
Abstract
A vine copula model is a flexible high-dimensional dependence model which uses only bivariate building blocks. However, the number of possible configurations of a vine copula grows exponentially as the number of variables increases, making model selection a major challenge in development. In this work, we formulate a vine structure learning problem with both vector and reinforcement learning representation. We use neural network to find the embeddings for the best possible vine model and generate a structure. Throughout experiments on synthetic and real-world datasets, we show that our proposed approach fits the data better in terms of log-likelihood. Moreover, we demonstrate that the model is able to generate high-quality samples in a variety of applications, making it a good candidate for synthetic data generation.
Cite
@article{arxiv.1812.01226,
title = {Learning Vine Copula Models For Synthetic Data Generation},
author = {Yi Sun and Alfredo Cuesta-Infante and Kalyan Veeramachaneni},
journal= {arXiv preprint arXiv:1812.01226},
year = {2018}
}