Past exam of the mathematics course of the University of Cambridge / 2016 / iii / Paper 208 / 5 / b

The support condition for importance sampling is missing from the printed assertion. Assume $M>1$, $E_f|h|<\infty$, and $Mg(z)>f(z)$ for $f$-almost every $z$. For a proposal $Z\sim g$, rejection has conditional probability $1-f(Z)/(Mg(Z))$. Its unconditional probability is $(M-1)/M$, so the conditional density of a rejected draw isThus the rejected-draw weight is precisely $f(z)/r(z)$, andConditionally on any positive rejection count $n$ among $N$ independent proposals, the rejected locations have this iid conditional density. Henceis an unbiased estimator conditionally on $n$, and therefore also conditionally on the event $0<n<N$. This is importance sampling from the rejected-proposal distribution.

The nonstrict assumption $f\le Mg$ alone does not suffice: the rejected distribution may completely miss part of the target support. For example, let $g$ be uniform on $[0,1]$, $f(z)=21_{[0,1/2]}(z)$, $M=2$, and $h=1$. All rejected proposals lie in $(1/2,1]$, where $f=0$, so the displayed estimator is always $0$, whereas $E_{f}h=1$. This remains a counterexample after conditioning on $0<n<N$. A strict envelope, such as choosing $M$ larger than the essential supremum of $f/g$, repairs the missing support condition. Near-equality can still give high-variance weights, even when it remains an unbiased estimator.