Monte Carlo and quasi-Monte Carlo integration for likelihood functions

TL;DR

This paper compares Monte Carlo and quasi-Monte Carlo methods for likelihood function integration, analyzing their errors and scaling behavior with data points and dimension, revealing conditions where QMC outperforms MC.

Contribution

It provides a detailed comparison of MC and QMC integration errors, including new bounds on high-dimensional star discrepancy for Halton sequences.

Findings

QMC outperforms MC when certain logarithmic and data-dependent factors are small.

Both methods exhibit poor scaling in very high dimensions.

QMC's advantage depends on the relationship between m, n, and p.

Abstract

We compare the integration error of Monte Carlo (MC) and quasi-Monte Carlo (QMC) methods for approximating the normalizing constant of posterior distributions and certain marginal likelihoods. In doing so, we characterize the dependency of the relative and absolute integration errors on the number of data points ($n$), the number of grid points ($m$) and the dimension of the integral ($p$). We find that if the dimension of the integral remains fixed as $n$ and $m$ tend to infinity, the scaling rate of the relative error of MC integration includes an additional $n^{1/2}\log(n)^{p/2}$ data-dependent factor, while for QMC this factor is $\log(n)^{p/2}$. In this scenario, QMC will outperform MC if $\log(m)^{p - 1/2}/\sqrt{mn\log(n)} < 1$, which differs from the usual result that QMC will outperform MC if $\log(m)^p/m^{1/2} < 1$.The accuracies of MC and QMC methods are also examined in the high-dimensional setting as $p \rightarrow \infty$, where MC gives more optimistic results as the scaling in dimension is slower than that of QMC when the Halton sequence is used to construct the low discrepancy grid; however both methods display poor dimensional scaling as expected. An additional contribution of this work is a bound on the high-dimensional scaling of the star discrepancy for the Halton sequence.