Alphabeta Math
TheoremStatement: Literature-sourcedProof: AI-adaptedPipeline-generatedprecheck passjudge pass (gpt-6-luna)
How statement and proof provenance work

The first chip identifies the source of the statement or construction; the second identifies the source of its local proof or verification.

  • Literature-sourced: the exact statement appears in a cited source; only wording and notation differ.
  • AI-adapted: a semantically identical restatement of literature-sourced material, modulo indexing, notation, and boundary cases adopted by the library.
  • AI-generated: a genuinely novel statement formulated by AI, with no source for the claim itself.

These labels describe origin, not correctness: citations and verification chips remain separate evidence.

Expected proposal count under a finite rejection envelope

Statement

In the setup of Rejection sampling has the target acceptance law, let N be the number of proposals through and including the first accepted proposal. With p=Zf/(cZg) and q=1−p, for every integer k≥1, P(N=k)=qk−1p,E[N]=1p=cZgZf. Thus N is geometric on {1,2,…}. For normalized target and proposal densities, E[N]=c.

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.

[F1]

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.

[F2]

Rejection sampling has the target acceptance law: each proposal is accepted with probability p=Zf/(cZg)∈(0,1].

[F3]

Expectation of a nonnegative or integrable random variable: the expectation of a nonnegative random variable is its extended integral.

[F4]

Additivity of the nonnegative Lebesgue integral: the integral of a finite sum of nonnegative measurable functions is the sum of their integrals.

[F5]

Monotone convergence for the integral: increasing nonnegative measurable truncations converge in integral to their pointwise limit.

[F6]

For ∣r∣<1, ∑k≥0rk=1/(1−r), and for ∣r∣≥1 the series diverges: for 0≤q<1, ∑j=0∞qj=1/(1−q), including q=0 with q0=1.

Proof

Given: The setup in the Statement.

1.1F1F2givenalgebra

Let Ai be the event that proposal i 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 Ai are IID. By [F2], P(Ai)=p for every i. Therefore P(N>m)=qm for m≥0, and P(N=k)=P(A1c∩⋯∩Ak−1c∩Ak)=qk−1p for each k≥1.

2.1F3F4step 1.1algebra

For each m≥1, the pointwise identity N∧m=∑j=1m1{N≥j} holds, including on the null event N=∞. By [F3], [F4], and step 1.1, E[N∧m]=∑j=1mP(N≥j)=∑j=0m−1qj.

3.1F5F6step 2.1algebra

The truncations N∧m increase pointwise to N. By [F5] and then [F6], E[N]=lim⁡m→∞E[N∧m]=∑j=0∞qj=11−q=1p. This is finite because p>0.

4.1F2step 3.1algebra∎

Substituting p=Zf/(cZg) from [F2] gives E[N]=cZg/Zf; when Zf=Zg=1, this is c.

Depends on

Used by

Dependency tree · two levels

49 results within two dependency steps of this one, each drawn at its shortest distance from it. An arrow runs from a result to what uses it, so the chart reads left to right and ends at this result, which carries a heavier outline. Every node is a link to that result. Click elsewhere on the chart to enlarge it.

Sources