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.
Characteristic functions of bernoulli binomial and poisson laws
Example
For , and , the laws with masses have characteristic functions , , and , respectively. Zeroth powers, including in these finite combinatorial formulas, mean the empty product one. For a finite mixture with and , one also has .
Facts & Assumptions
Given: The hypotheses and conventions in the example.
The transform integrates exp(itx), which has modulus one. Characteristic function of a real random variable.
Independent sums have product characteristic functions. Characteristic functions under affine maps and independent sums.
Complex exponential addition includes its real extension. , and the complex exponential extends the real exponential.
The defining exponential series converges absolutely at every complex argument. The complex exponential series converges absolutely for every complex argument.
The finite binomial expansion holds over complex scalars. The binomial theorem over the complex field.
Integration commutes with finite complex linear combinations. The Lebesgue integral is linear on .
Nonnegative countable weighted sums of measures are measures. Nonnegative scalar multiples and countable weighted sums of measures are measures.
Each real point supplies a Dirac probability. A Dirac set function is a probability measure.
Bounded pointwise approximation can pass through a finite-measure integral. Dominated convergence.
Verification
All displayed masses are nonnegative. The Bernoulli masses sum to one. The binomial sum is , including , when there is exactly one term of value one. The Poisson sum is . Weighted Dirac sums therefore define Borel probabilities on . For any such countably supported law with masses at distinct integers, tends to almost everywhere for that law and satisfies . Its integral is the finite sum of values times singleton masses. DCT yields , with absolute sum . Finite supports are the same calculation with zero masses afterwards.
Substitute the Bernoulli masses to get . For the binomial law the finite sum is . This is also the product supplied for any already-given family of independent Bernoulli variables; no existence of an infinite family is needed. For Poisson, absolute convergence allows recognition of the defining series: . At the first two laws are ; at they are and ; and give . The stated formulas give precisely their constant-point transforms, and all values at equal one.
For the finite mixture, the weighted-sum theorem gives a measure of total mass . For a simple complex function on a disjoint measurable partition, the integral definition and finite sums give . Approximate by rounding its real and imaginary parts down to multiples of ; each approximation is Borel, simple, uniformly bounded by three and converges pointwise. DCT for and for each of the finitely many 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.
Depends on
- Characteristic function of a real random variable
- Characteristic functions under affine maps and independent sums
- $\exp(z+w)=\exp z\,\exp w$, and the complex exponential extends the real exponential
- The complex exponential series converges absolutely for every complex argument
- The binomial theorem over the complex field
- The Lebesgue integral is linear on $L^1(\mu)$
- Nonnegative scalar multiples and countable weighted sums of measures are measures
- A Dirac set function is a probability measure
- Dominated convergence
Used by
Nothing in the library uses this result yet.
Dependency tree · two levels
40 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
- Durrett, Probability: Theory and Examples, fifth edition (standard reference, not scraped)
- Norris, Probability and Measure (standard reference, not scraped)