Simulation and transforms

Simulation

Vine models support ordinary random simulation through rand and quasi-Monte Carlo simulation through simulate_qmc.

U = rand(vine, 10_000)
Q = simulate_qmc(vine, 10_000)

Rosenblatt transform

For a fitted or explicit vine , the Rosenblatt transform maps dependent observations to conditionally uniform coordinates:

Z = rosenblatt(vine, U)

The inverse transform reconstructs observations from independent uniforms:

U2 = inverse_rosenblatt(vine, Z)
maximum(abs.(U2 .- U))

In-place variants are available for repeated workloads:

rosenblatt!(dest, vine, U)
inverse_rosenblatt!(dest, vine, Z)

For standard general R-vines truncated below full depth, density evaluation and fitting are available, but Rosenblatt/inverse Rosenblatt transforms are not yet implemented. Because rand, simulate_qmc, and the numerical CDF use the inverse Rosenblatt transform, those operations currently require a full-depth standard general R-vine. Truncated C- and D-vines are not subject to this limitation.

Numerical CDF

The multivariate CDF is evaluated numerically for general vines. Use set_cdf_nsamples! to control the integration budget and enable_deterministic_cdf! when reproducibility is more important than randomization.