Alphabeta Math
ExampleConstruction: AI-adaptedVerification: AI-adaptedPipeline-generatedjudge pass (gpt-5.6-terra)
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Characteristic functions of bernoulli binomial and poisson laws

Example

For 0p1, nN and λ0, the laws with masses Bern(p)=(1p)δ0+pδ1,Bin(n,p){k}=(nk)pk(1p)nk (0kn),Pois(λ){k}=eλλkk! (k0) have characteristic functions 1p+peit, (1p+peit)n, and exp(λ(eit1)), respectively. Zeroth powers, including 00 in these finite combinatorial formulas, mean the empty product one. For a finite mixture ρ=j=1rajμj with aj0 and jaj=1, one also has φρ=jajφμj.

Facts & Assumptions

Given: The hypotheses and conventions in the example.

[F1]

The transform integrates exp(itx), which has modulus one. Characteristic function of a real random variable.

[F2]

Independent sums have product characteristic functions. Characteristic functions under affine maps and independent sums.

[F4]

The defining exponential series converges absolutely at every complex argument. The complex exponential series converges absolutely for every complex argument.

[F5]

The finite binomial expansion holds over complex scalars. The binomial theorem over the complex field.

[F6]

Integration commutes with finite complex linear combinations. The Lebesgue integral is linear on L1(μ).

[F7]

Nonnegative countable weighted sums of measures are measures. Nonnegative scalar multiples and countable weighted sums of measures are measures.

[F8]

Each real point supplies a Dirac probability. A Dirac set function is a probability measure.

[F9]

Bounded pointwise approximation can pass through a finite-measure integral. Dominated convergence.

Verification

technique · direct
1.1

All displayed masses are nonnegative. The Bernoulli masses sum to one. The binomial sum is (p+1p)n=1, including n=0, when there is exactly one term of value one. The Poisson sum is eλk0λk/k!=eλeλ=1. Weighted Dirac sums therefore define Borel probabilities on R. For any such countably supported law with masses bk at distinct integers, fN(x)=k=0Neitk1{k}(x) tends to eitx almost everywhere for that law and satisfies fN1. Its integral is the finite sum of values times singleton masses. DCT yields φ(t)=k0bkeitk, with absolute sum bk=1. Finite supports are the same calculation with zero masses afterwards.

F1F3F4F5F7F8F9
2.1

Substitute the Bernoulli masses to get (1p)+peit. For the binomial law the finite sum is k=0n(nk)(peit)k(1p)nk=(1p+peit)n. This is also the product supplied for any already-given family of n independent Bernoulli variables; no existence of an infinite family is needed. For Poisson, absolute convergence allows recognition of the defining series: eλk0(λeit)k/k!=eλexp(λeit)=exp(λ(eit1)). At p=0 the first two laws are δ0; at p=1 they are δ1 and δn; n=0 and λ=0 give δ0. The stated formulas give precisely their constant-point transforms, and all values at t=0 equal one.

step 1.1F2F3F4F5
3.1

For the finite mixture, the weighted-sum theorem gives a measure of total mass jaj=1. For a simple complex function s==1qc1E on a disjoint measurable partition, the integral definition and finite sums give sdρ=jajsdμj. Approximate eitx by rounding its real and imaginary parts down to multiples of 2N; each approximation is Borel, simple, uniformly bounded by three and converges pointwise. DCT for ρ and for each of the finitely many μj passes the simple identity to the limit, giving the mixture formula. Zero weights contribute zero, a one-component mixture returns that component, and no empty mixture has weights summing to one. No AC is used in these explicit sums and approximations.

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Depends on

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Sources