Let Omega=N_0^2, A=Pot(Omega) and P the product of two poisson distributions with paremeter lambda_1, λ>0, i.e

wasangagac4

wasangagac4

Answered question

2022-10-21

Let Ω = N 0 2 , A = P o t ( Ω ) and P the product of two poisson distributions with paremeter λ 1 , λ > 0, i.e
P ( { ( n 1 , n 2 ) } ) = λ 1 n 1 λ 2 n 2 n 1 ! n 2 ! e λ 1 λ 2
Define then X : Ω N 0 ,   ( n 1 , n 2 ) n 1 + n 2 . Prove that X is poisson distributed with parameter λ 1 + λ 2

Answer & Explanation

erkvisin7s

erkvisin7s

Beginner2022-10-22Added 12 answers

Step 1
Your probability space is explicitly the product space ( N 0 × N 0 , P ( N 0 ) P ( N 0 ) , P λ 0 × P λ 1 ), where P λ is the Poisson probability measure on ( N 0 , P ( N 0 ) ). Since you have explicitly defined probabilities on the non-negative integers, you don't need a random variable to map from another underlying probability space: you already have P λ 0 ( { n 1 } ) = λ 1 n 1 n 1 ! e λ 1 for all n 1 N 0 .
Step 2
Technical point: Remember that a probability measure is defined on sets in the sigma-algebra. For a discrete measure, it is sufficient to define it on the singletons { { n } : n N 0 }.)
Thus, to "fix" the notation in your proof, we can write
P ( X = n ) = P ( { ( n 1 , n 2 ) : X ( n 1 , n 2 ) = n } ) = P ( { ( n 1 , n 2 ) : n 1 + n 2 = n } ) = P ( k = 0 n { ( n 1 , n 2 ) : n 1 = k , n 2 = n k } ) =
The first line simply clarifies what the usual notation P ( X = n ) means: it is the probability of the event where the random variable takes on a certain value (i.e. set of 2-tuples ( n 1 , n 2 ) that add to n). The rest continues as you have written. After splitting into a sum (by additivity), for each 0 k n we will have P ( { ( n 1 , n 2 ) : n 1 = k , n 2 = n k } ) = P λ 1 ( { k } ) P λ 2 ( { n k } ) since we have a product measure.
This notation is indeed not as easy to understand as the usual random variable notation (which allows for a more "probabilistic" way of thinking). By abstracting away the underlying probability space, we can simply say that X 1 P o i s s o n ( λ 1 ) and X 2 P o i s s o n ( λ 2 ) are two independent random variables on the same probability space, so that for X = X 1 + X 2 we can simply write P ( X = n ) = P ( X 1 + X 2 = n ) = k = 0 n P ( X 1 = k , X 2 = n k ) = . The key takeaway is that product measures are essentially the same as independent random variables.
Gisselle Hodges

Gisselle Hodges

Beginner2022-10-23Added 5 answers

Step 1
P ( X = n ) = P ( { ( n 1 , n 2 ) : n 1 + n 2 = n } ) (1) = P ( { ( n 1 , n 2 ) : n 1 + n 2 = n } k = 0 { ( n 1 , n 2 ) : n 1 = k } ) (2) = P ( k = 0 ( { ( n 1 , n 2 ) : n 1 + n 2 = n } { ( n 1 , n 2 ) : n 1 = k } ) ) (3) = P ( k = 0 n ( { ( n 1 , n 2 ) : n 1 + n 2 = n } { ( n 1 , n 2 ) : n 1 = k } ) ) = P ( k = 0 n { ( n 1 , n 2 ) : n 1 = k , n 2 = n k } ) (4) = k = 0 n P ( { ( n 1 , n 2 ) : n 1 = k , n 2 = n k } ) = k = 0 n P ( { ( k , n k ) } ) (5) = k = 0 n λ 1 k λ 2 n k k ! ( n k ) ! e ( λ 1 + λ 2 ) = ( λ 1 + λ 2 ) n n ! e ( λ 1 + λ 2 ) .
This shows X Poisson ( λ 1 + λ 2 )
Step 2
Details explanation:
(1): because Ω = k = 0 { ( n 1 , n 2 ) : n 1 = k } )
(2): set operation.
(3): when k > n, the intersection is .
(4): These n sets are disjoint.
(5): Use the specification of P directly.

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