Poisson Gamma Bayesian Statistics

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I have a Bayesian stat question where I am a little confused.

Let $N_{1}$ and $N_{2}$ independent having $\mbox{Poisson}(\Lambda)$ distribution and $\Lambda \sim \mbox{Gamma}(\alpha,\theta)$. Then how we prove that $N_{1} + N_{2} \sim \mbox{Poisson}(2 \Lambda)$ because it could be the case that $N_{1} \sim \mbox{Poisson}(2)$ and $N_{2} \sim \mbox{Poisson}(17)$?

Thank you!

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To avoid ambiguity, it would be preferable to write the given as

$$N_i|\Lambda=\lambda \quad \overset{iid}{\sim } Poiss(\lambda),$$

and the claim you are trying to show as

$$N_1+N_2|\Lambda=\lambda \quad \overset{iid}{\sim } Poiss(2\lambda).$$

Then the claim is easily shown using characteristic functions:

$$\varphi_{N_1+N_2|\Lambda =\lambda}(t)=\varphi_{N_1|\Lambda =\lambda}(t)\varphi_{N_2|\Lambda =\lambda}(t)=(\exp(\lambda (e^{it}-1)))^2=\exp(2\lambda (e^{it}-1)).$$

The distribution of $\Lambda$ is irrelevant.