Scalable Maximum Entropy Population Synthesis via Persistent Contrastive Divergence
Maximum entropy (MaxEnt) modelling provides a principled framework for generating synthetic populations from aggregate census data, without access to individual-level microdata. The bottleneck of exact-enumeration approaches is expectation computation by explicit summation over the full tuple space $\cX$, which becomes infeasible for more than $K \approx 20$ categorical attributes; sampling-based alternatives exist but rely on Metropolis-type schemes that require proposal tuning and rejection steps. We propose \emph{GibbsPCDSolver}, a stochastic replacement for this computation based on Persistent Contrastive Divergence (PCD): a persistent pool of $N$ synthetic individuals is updated by G...
| Authors | Mirko Degli Esposti |
|---|---|
| Primary category | cs.LG |
| Published | 28 March 2026 |
| Last updated at source | 17 April 2026 |
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