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Hello I am trying to solve a homework of Poisson Process and I have a few about Poisson decomposition I cant solve.

I have a Poisson process $N(t)$ with $\lambda=100$/hour which decomposes into 2 Poisson processes $N_1(t)$ and $N_2(t)$ with probability $0.7$ and $0.3$ respectively. It basically means that each event on my Poisson process N(t) has two posibilities, being counted with the Poisson process N1(t) or being counted with the poisson process N2(t). A common example is that people arrive to a hospital with a Poisson process N(t) with certain rate. Once there, they can be hospitalized or not with probabilities P and (1-P). So N1(t) counts the hospitalized and N2(t) the ones who were not hospitalized.

I have to solve the following problems.

1) Obtain expression for $E[N(1) | N_2(1)>2]$

2) For $0 < t_1 < t_2 < t$: Solve: $E[N_1(t) | N(t_2) - N(t_1) = n]$

I will be very grateful for any help! Greetings

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  • $\begingroup$ The part "which decomposes into N1(t) and N2(t) with probability 0.7 and 0.3 respectively" is unclear. Please explain. $\endgroup$
    – Did
    Sep 25, 2016 at 22:57
  • $\begingroup$ I tried to explain better on the edit. $\endgroup$
    – Joe Cox
    Sep 25, 2016 at 23:13
  • $\begingroup$ I guess you are talking about splitting a Poisson process. Note that $N_1$ and $N_2$ are independent, and $N = N_1 + N_2$, therefore the first one you can obtain rather easily (just need to calculate the truncated Poisson expectation). The second one you just need to split the time interval, and use the independent increment argument. For the middle overlapping interval, you should look for the Binomial distribution. $\endgroup$
    – BGM
    Sep 26, 2016 at 9:30
  • $\begingroup$ Thank you very much, I now understand the theory behind the questions. I´ve been trying to solve them, but I haven´t been able to get any concrete answers. $\endgroup$
    – Joe Cox
    Sep 26, 2016 at 15:19

1 Answer 1

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Notice for $t>0$ we have $N_1(t) \sim \text{Poisson}(70t)$, $N_2(t) \sim \text{Poisson}(30t)$, and $$N(t)=N_1(t)+N_2(t) \sim \text{Poisson}(100t)$$ Moreover, $$E(N(1)|N_2(1)>2)=\sum_{k=3}^{\infty}k \cdot \frac{P(N(1)=k,N_2(1)>2)}{P(N_2(1)>2)}$$ Also note for $k\geq 3$ $$\{N(1)=k\}\cap \{N_2(1)>2\}=\coprod_{j=3}^k\{N_1(1)=k-j\}\cap\{N_2(1)=j\}$$ Since the union on the right hand side is disjoint, $$P(N(1)=k,N_2(1)>2)=\frac{e^{-100}}{k!}\sum_{j=3}^k{k \choose j}30^j70^{k-j}$$ Using the Binomial Theorem, $$P(N(1)=k,N_2(1)>2)=e^{-100}\frac{100^k}{k!}\Bigg(1-(0.7)^k\bigg[1+\frac{3k}{7}+\frac{9k(k-1)}{98}\bigg]\Bigg)$$ Meanwhile, $$P(N_2(1)>2)=1-P(N_2(1)\in \{0,1,2\})=1-481e^{-30}$$ Wolfram Alpha says that $$E(N(1)|N_2(1)>2)=\sum_{k=3}^{\infty}k \cdot \frac{P(N(1)=k,N_2(1)>2)}{P(N_2(1)>2)}=\frac{100(e^{30}-346)}{e^{30}-481}\approx 100$$ For part (b) the expected number of arrivals by $N_1$ on $\big[0,t_1\big]\dot\cup\big[t_2,t\big]$ is $70(t+t_1-t_2)$ and since $$N_1(t_2)-N_1(t_1)|N(t_2) - N(t_1)=n \sim \text{Binomial}\Big(n,\frac{7}{10}\Big)$$ we see that $$E(N_1(t)|N(t_2)-N(t_1)=n)=\frac{7n}{10}+70(t+t_1-t_2)$$

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