Generation
Relation Modeling
Capture and reproduce the complex inter-variable relationships that give real data its structure and meaning.
Preserving Structure
Multivariate Dependencies
Model correlations and conditional dependencies across columns to preserve joint distributions.
Temporal Relations
Handle time-series patterns and sequential dependencies so that generated data reflects realistic temporal dynamics.
Causal Structure
Encode causal graphs that ensure synthetic data respects directional influences observed in the real world.
