Product Monte Carlo (PMC)

PMC estimates expectations under a product of densities. To see how, consider the following setup.

Suppose you can draw samples independently from $K$ distributions on $\mathcal X\subseteq\mathbb R^p$:

We define the product distribution $\pi^\star$ as follows:

Given a function $h:\mathcal X\to\mathbb R$, we wish to compute its expectation under $\pi^\star$:

We call a function $c:\mathcal X^K\to\mathcal X$ a consensus function if

We call a function $d:\mathcal X^K\to\mathbb R^m$ a discrepancy function if, for all $x_1,\ldots,x_K\in\mathcal X$,

Let $C=c(X_1,\ldots,X_K)$ and $D=d(X_1,\ldots,X_K)$. Then, under suitable regularity conditions, we have that

PMC then works as follows:

1. Draw $S$ independent samples per distribution.
2. For each set of $K$ draws, compute its consensus $C$ and discrepancy $D$.
3. Fit a regressor to predict $h(C)$ from $D$.
4. Estimate the target expectation by evaluating the fitted regressor at $D=\mathbf 0_m$.

Install

To install PMC from PyPI, run:

pip install product-monte-carlo

Example

The example below shows how to estimate an expectation with PMC.

# Import PyTorch and PMC classes
import torch
from pmc import AnchoredDifferences, Mean, OLS, PMC

# Draw S = 1,000 samples from K = 2 univariate standard Gaussian distributions (p = 1)
samples = torch.randn(1_000, 2, 1)

# Configure PMC to estimate the mean under the product distribution
PMC_estimator = PMC(
    consensus=Mean(),
    discrepancy=AnchoredDifferences(),
    h=lambda x: x,
    regressor=OLS(),
)

# Compute and print the resulting estimate
PMC_estimate = PMC_estimator.estimate(samples)
print(PMC_estimate)