title: Compound Poisson process
text: # Compound Poisson process
  
  A compound Poisson process is a continuous-time stochastic process with jumps. The jumps arrive randomly according to a Poisson process and the size of the jumps is also random, with a specified probability distribution. To be precise, a compound Poisson process, parameterised by a rate $\lambda >0$ and jump size distribution G, is a process $\{\,Y(t):t\geq 0\,\}$ given by
  
      $Y(t)=\sum _{i=1}^{N(t)}D_{i}$
  
  where, $\{\,N(t):t\geq 0\,\}$ is the counting variable of a Poisson process with rate $\lambda$, and $\{\,D_{i}:i\geq 1\,\}$ are independent and identically distributed random variables, with distribution function G, which are also independent of $\{\,N(t):t\geq 0\,\}.\,$
  
  When $D_{i}$ are non-negative integer-valued random variables, then this compound Poisson process is known as a stuttering Poisson process.
  
  ## Properties of the compound Poisson process
  
  The expected value of a compound Poisson process can be calculated using a result known as Wald's equation as:
  
      $\operatorname {E} (Y(t))=\operatorname {E} (D_{1}+\cdots +D_{N(t)})=\operatorname {E} (N(t))\operatorname {E} (D_{1})=\operatorname {E} (N(t))\operatorname {E} (D)=\lambda t\operatorname {E} (D).$
  
  Making similar use of the law of total variance, the variance can be calculated as:
  
      ${\begin{aligned}\operatorname {var} (Y(t))&=\operatorname {E} (\operatorname {var} (Y(t)\mid N(t)))+\operatorname {var} (\operatorname {E} (Y(t)\mid N(t)))\\[5pt]&=\operatorname {E} (N(t)\operatorname {var} (D))+\operatorname {var} (N(t)\operatorname {E} (D))\\[5pt]&=\operatorname {var} (D)\operatorname {E} (N(t))+\operatorname {E} (D)^{2}\operatorname {var} (N(t))\\[5pt]&=\operatorname {var} (D)\lambda t+\operatorname {E} (D)^{2}\lambda t\\[5pt]&=\lambda t(\operatorname {var} (D)+\operatorname {E} (D)^{2})\\[5pt]&=\lambda t\operatorname {E} (D^{2}).\end{aligned}}$
  
  Lastly, using the law of total probability, the moment generating function can be given as follows:
  
      $\Pr(Y(t)=i)=\sum _{n}\Pr(Y(t)=i\mid N(t)=n)\Pr(N(t)=n)$
      ${\begin{aligned}\operatorname {E} (e^{sY})&=\sum _{i}e^{si}\Pr(Y(t)=i)\\[5pt]&=\sum _{i}e^{si}\sum _{n}\Pr(Y(t)=i\mid N(t)=n)\Pr(N(t)=n)\\[5pt]&=\sum _{n}\Pr(N(t)=n)\sum _{i}e^{si}\Pr(Y(t)=i\mid N(t)=n)\\[5pt]&=\sum _{n}\Pr(N(t)=n)\sum _{i}e^{si}\Pr(D_{1}+D_{2}+\cdots +D_{n}=i)\\[5pt]&=\sum _{n}\Pr(N(t)=n)M_{D}(s)^{n}\\[5pt]&=\sum _{n}\Pr(N(t)=n)e^{n\ln(M_{D}(s))}\\[5pt]&=M_{N(t)}(\ln(M_{D}(s)))\\[5pt]&=e^{\lambda t\left(M_{D}(s)-1\right)}.\end{aligned}}$
  
  ## Exponentiation of measures
  
  Let N, Y, and D be as above. Let μ be the probability measure according to which D is distributed, i.e.
  
      $\mu (A)=\Pr(D\in A).\,$
  
  Let $δ_0$ be the trivial probability distribution putting all of the mass at zero. Then the probability distribution of Y(t) is the measure
  
      $\exp(\lambda t(\mu -\delta _{0}))\,$
  
  where the exponential exp(ν) of a finite measure ν on Borel subsets of the real line is defined by
  
      $\exp(\nu )=\sum _{n=0}^{\infty }{\nu ^{*n} \over n!}$
  
  and
  
      $\nu ^{*n}=\underbrace {\nu *\cdots *\nu } _{n{\text{ factors}}}$
  
  is a convolution of measures, and the series converges weakly.
  
  