This paper introduces a multivariate sparse multiscale Bernstein polynomial model for copula dependence structures, utilizing a Bayesian spike-and-slab prior. The method enhances efficiency by preserving significant components, reducing computational demands, and enabling practical applications in multivariate density estimation, particularly for financial risk forecasting.
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