Math 2.0

Probability theory and stochastic processes Proof

Proof of the sum of independent Poisson random variables by the multinomial theorem

Builds on Poisson distributionIndependence of random variablesMultinomial theoremMultinomial coefficientProbability mass function

Proof

We compute the probability mass function of the sum directly.

  1. Let S=X1+⋯+XnS = X_1 + \dots + X_n and Λ=λ1+⋯+λn\Lambda = \lambda_1 + \dots + \lambda_n. Then Λ>0\Lambda > 0.
  2. By the definition of the Poisson distribution, P(Xi=k)=e−λiλik/k!P(X_i = k) = e^{-\lambda_i} \lambda_i^k / k! for each integer k≥0k \ge 0. These values add up to 11, so Xi∈{0,1,2,… }X_i \in \set{0, 1, 2, \dots} almost surely.
  3. Let NN be the event that every XiX_i is a nonnegative integer. By step 2, its complement is a finite union of null events, so P(N)=1P(N) = 1.
  4. On NN, the sum SS is a nonnegative integer. So S∈{0,1,2,… }S \in \set{0, 1, 2, \dots} almost surely.
  5. Fix an integer m≥0m \ge 0. Let KmK_m be the set of nn-tuples (k1,…,kn)(k_1, \dots, k_n) of nonnegative integers with k1+⋯+kn=mk_1 + \dots + k_n = m. It is a finite set.
  6. Up to a null event, {S=m}\set{S = m} is the disjoint union over (k1,…,kn)∈Km(k_1, \dots, k_n) \in K_m of the events {X1=k1,…,Xn=kn}\set{X_1 = k_1, \dots, X_n = k_n}. So P(S=m)=∑KmP(X1=k1,…,Xn=kn)P(S = m) = \sum_{K_m} P(X_1 = k_1, \dots, X_n = k_n).
  7. By independence with Bi={ki}B_i = \set{k_i}, each term is ∏i=1nP(Xi=ki)=e−Λ∏i=1nλiki/ki!\prod_{i=1}^{n} P(X_i = k_i) = e^{-\Lambda} \prod_{i=1}^{n} \lambda_i^{k_i} / k_i!.
  8. Multiply and divide by m!m!. Then P(S=m)=e−Λm!∑(k1,…,kn)∈Kmm!k1!⋯kn!λ1k1⋯λnkn.P(S = m) = \frac{e^{-\Lambda}}{m!} \sum_{(k_1, \dots, k_n) \in K_m} \frac{m!}{k_1! \cdots k_n!} \lambda_1^{k_1} \cdots \lambda_n^{k_n}.
  9. The fraction m!/(k1!⋯kn!)m! / (k_1! \cdots k_n!) is the multinomial coefficient. By the multinomial theorem, the sum equals (λ1+⋯+λn)m=Λm(\lambda_1 + \dots + \lambda_n)^m = \Lambda^m.
  10. So P(S=m)=e−ΛΛm/m!P(S = m) = e^{-\Lambda} \Lambda^m / m! for each integer m≥0m \ge 0. With step 4, the probability mass function of SS is that of Poisson⁡(Λ)\operatorname{Poisson}(\Lambda).
  11. Hence X1+⋯+Xn∼Poisson⁡(λ1+⋯+λn)X_1 + \dots + X_n \sim \operatorname{Poisson}(\lambda_1 + \dots + \lambda_n).

Idea

The sum equals mm exactly when the counts k1,…,knk_1, \dots, k_n add up to mm. Independence turns each such case into a product of Poisson weights, and the multinomial theorem collects all the cases into Λm\Lambda^m.

Reading path

To understand this page from the ground up, read these 63 pages in order. The foundations come first. The path takes the proofs Proof of Pascal's rule from the recursive definition, Proof of the factorial formula for binomial coefficients by induction, Proof of the binomial theorem by induction, Proof of the multinomial theorem by induction on the number of terms.

  1. Binomial coefficient definition
  2. Construction of the reals from Cauchy sequences concept
  3. Element definition
  4. Factorial definition
  5. Group concept
  6. Integers object
  7. Multinomial coefficient definition
  8. Ordered field definition
  9. Proposition concept
  10. Random experiment concept
  11. Rational numbers object
  12. Real numbers object
  13. Proof of Pascal's rule from the recursive definition proof
  14. Set definition
  15. Ring definition
  16. Ring of integers statement
  17. Disjunction definition
  18. Implication definition
  19. Predicate definition
  20. Pascal's rule statement
  21. Ordered pair definition
  22. Commutative ring definition
  23. Union definition
  24. Sample space definition
  25. Existential quantifier definition
  26. Universal quantifier definition
  27. The rationals form a field statement
  28. Cartesian product definition
  29. Proof of the factorial formula for binomial coefficients by induction proof
  30. Subset definition
  31. Uniqueness quantifier definition
  32. The rationals form an ordered field statement
  33. Binary relation definition
  34. Factorial formula for binomial coefficients statement
  35. Universal set definition
  36. Power set definition
  37. Order on the reals definition
  38. Proof of the binomial theorem by induction proof
  39. Function definition
  40. Complement definition
  41. Interval definition
  42. Binomial theorem statement
  43. Injective function definition
  44. Preimage of a set definition
  45. Sigma-algebra definition
  46. Countable set definition
  47. Proof of the multinomial theorem by induction on the number of terms proof
  48. Event definition
  49. Borel sigma-algebra definition
  50. Probability axioms concept
  51. Multinomial theorem statement
  52. Probability measure definition
  53. Probability space definition
  54. Conditional probability definition
  55. Random variable definition
  56. Discrete random variable definition
  57. Independence of two events definition
  58. Probability mass function definition
  59. Mutual independence of events definition
  60. Poisson distribution definition
  61. Independent sigma-algebras definition
  62. Independence of random variables definition
  63. Proof of the sum of independent Poisson random variables by the multinomial theorem proof

Where this leads

1 page builds on this one.

Further reading

  1. Web https://en.wikipedia.org/wiki/Poisson_distribution, Sums of Poisson-distributed random variables
About this page
TagECJEG3JHVM
URIm2://ECJEG3JHVM
Kindproof
WritingW0 stub
MSC60E05
ProvesSum of independent Poisson random variables
UsesPoisson distribution, Independence of random variables, Multinomial theorem, Multinomial coefficient, Probability mass function
Changed4 November 2026

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