Product Monte Carlo: Expectation Under a Product of Densities
Product Monte Carlo (PMC) is an algorithm designed to calculate expectations under a product of probability densities, which is common in areas like federated learning. It achieves this by transforming the difficult product expectation into a regression problem using computable conditional expectations based on consensus and discrepancy mappings. The method provides accurate estimates with principled uncertainty quantification and scales better than existing methods.