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Expected proposal count under a finite rejection envelope
Statement
In the setup of Rejection sampling has the target acceptance law, let be the number of proposals through and including the first accepted proposal. With and , for every integer , Thus is geometric on . For normalized target and proposal densities, .
Facts & Assumptions
Given: The envelope, normalizers, and ideal IID draw stream in Rejection sampling, with the acceptance probability established by Rejection sampling has the target acceptance law.
Rejection sampling and Identical distribution and IID families: the proposal and auxiliary draws are independent across trials and share their respective laws, so the acceptance events are IID.
Rejection sampling has the target acceptance law: each proposal is accepted with probability .
Expectation of a nonnegative or integrable random variable: the expectation of a nonnegative random variable is its extended integral.
Additivity of the nonnegative Lebesgue integral: the integral of a finite sum of nonnegative measurable functions is the sum of their integrals.
Monotone convergence for the integral: increasing nonnegative measurable truncations converge in integral to their pointwise limit.
For , , and for the series diverges: for , , including with .
Proof
Given: The setup in the Statement.
Let be the event that proposal is accepted. The algorithm uses the same measurable acceptance region for each IID proposal-uniform pair, so mutual independence and common distribution in [F1] imply that the events are IID. By [F2], for every . Therefore for , and for each .
For each , the pointwise identity holds, including on the null event . By [F3], [F4], and step 1.1,
The truncations increase pointwise to . By [F5] and then [F6], This is finite because .
Substituting from [F2] gives ; when , this is .
Depends on
- Rejection sampling
- Additivity of the nonnegative Lebesgue integral
- Expectation of a nonnegative or integrable random variable
- Identical distribution and IID families
- For $|r| < 1$, $\sum_{k \ge 0} r^k = 1/(1-r)$, and for $|r| \ge 1$ the series diverges
- Monotone convergence for the integral
- Rejection sampling has the target acceptance law
Used by
Dependency tree · two levels
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Sources
- Art B. Owen, Monte Carlo Theory, Methods and Examples, Chapter 4 §4.7 (standard reference, not scraped)
- Computational Statistics with R, Chapter 6 §6.1 (standard reference, not scraped)