MHB Property of independent random variables

AI Thread Summary
The discussion focuses on the property of the sum of independent random variables, specifically how to derive the probability mass function for Z = X + Y, where X and Y are the outcomes of rolling two dice. The Law of Total Probability is utilized to express the probability of Z in terms of the probabilities of X and Y. The key challenge is understanding the summation limits for X, which depend on the fixed value of Z, leading to conditions that ensure valid outcomes for both dice. The final results show that for different ranges of Z, the probabilities can be calculated based on the number of valid combinations of X and Y. The explanation clarifies the initial confusion regarding the summation index and the reasoning behind the min and max conditions.
alfred2
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hello!

I'm trying to understand the following property:
Let X and Y be independent random variables z: = X + Y. Then
http://imageshack.us/a/img268/9228/71pe.png
where fZ (z) is the probability mass function for a discrete random variable defined as follows:

http://imageshack.us/a/img801/9218/q71.png

and Wx is the set of possible values ​​for the random variable X

Proof: Using the Law of total probability, which is this:
http://imageshack.us/a/img198/8461/qbn.png
we obtain
http://imageshack.us/a/img855/2067/mbd4.png
Question-I don't see how Law of total probability helps us. I even don't understand how we get the first line of the proof-End of Question

Then my book proposes this example: We roll two dices. Let X/Y random variables, which indicate the number of points in the first/second die. We calculate the density of Z: = X + Y:
http://imageshack.us/a/img10/7541/5fx.png
For 2 <= z <= 7 we obtain
http://imageshack.us/a/img17/9047/stvo.png
And 7 <z <= 12:
http://imageshack.us/a/img90/5522/fuz.png
Here I just get that:
http://imageshack.us/a/img593/7442/ok0i.png
But I do not understand why we have this index of summation nor why we put min and max in the next step
Could you help me please? Thank you!
 
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alfred said:
Then my book proposes this example: We roll two dices. Let X/Y random variables, which indicate the number of points in the first/second die. We calculate the density of Z: = X + Y:
http://imageshack.us/a/img10/7541/5fx.png
For 2 <= z <= 7 we obtain
http://imageshack.us/a/img17/9047/stvo.png
And 7 <z <= 12:
http://imageshack.us/a/img90/5522/fuz.png
Here I just get that:
http://imageshack.us/a/img593/7442/ok0i.png
But I do not understand why we have this index of summation nor why we put min and max in the next step
Could you help me please? Thank you!
The number of spots on the first die is $x$, and on the second die is $y$. You want to know when the sum $x+y$ (the total number of spots on the two dice) is equal to $z$.

If the sum of the spots on the two dice is to be $z$, given that $X=x$, then obviously $x$ must be at least $1$ (because that is the smallest possible value for $x$). But also $x$ must be at least $z-6$ (because $y = z-x$, and $y$ cannot be larger than $6$). Thus we must have $x\geqslant \max\{1,z-6\}$. Next, $x$ cannot be bigger than $6$ (obviously), but also $x$ must not be bigger than $z-1$ (because $y = z-x$, and $y$ cannot be less than $1$). Thus we must have $x\leqslant \min\{6,z-1\}$. Provided both those conditions hold, there will then be a probability of $1/6$ that $y$ will be equal to $z-x$. That is where the expression $$\sum_{x=\max\{1,z-6\}}^{\min\{6,z-1\}}\frac1{36}$$ comes from.

I think that if you follow this example carefully, you will begin to see how the proof in the first part of your post works.
 
alfred said:
Okay, so we have got as far as $$\text{Pr}[Z=z] = \frac16 \sum_{x=\max\{1,z-6\}}^{\min\{6,z-1\}}\text{Pr}[Y= z-x]$$. In that formula, $z$ is fixed. Once we are inside the summation, $x$ is also fixed, because at that stage we are dealing with what happens for a particular value of $x$. So $\text{Pr}[Y= z-x]$ is the probability that $y$ takes the fixed value $z-x$. And of course the probability that $y$ takes any given single value (in the range 1,...,6) is 1/6.

That gives the formula $$\text{Pr}[Z=z] = \sum_{x=\max\{1,z-6\}}^{\min\{6,z-1\}}\frac1{36} $$. Thus each term in the sum is equal to 1/36, and to evaluate the sum we have to multiply 1/36 by the number of terms. If $z$ lies between 2 and 7, then $\max\{1,z-6\} = 1$ and $\min\{6,z-1\} = z-1$. So the sum goes from $x=1$ to $x=z-1$. Hence there are $z-1$ terms in the sum, and since each term is equal to 1/36, the sum is $$\frac{z-1}{36}$$. In a similar way, you should be able to work out that if $z$ lies between 7 and 12 then the number of terms in the sum is $13-z$ and so their sum is $$\frac{13-z}{36}$$.
 
Opalg said:
Okay, so we have got as far as $$\text{Pr}[Z=z] = \frac16 \sum_{x=\max\{1,z-6\}}^{\min\{6,z-1\}}\text{Pr}[Y= z-x]$$. In that formula, $z$ is fixed. Once we are inside the summation, $x$ is also fixed, because at that stage we are dealing with what happens for a particular value of $x$. So $\text{Pr}[Y= z-x]$ is the probability that $y$ takes the fixed value $z-x$. And of course the probability that $y$ takes any given single value (in the range 1,...,6) is 1/6.

That gives the formula $$\text{Pr}[Z=z] = \sum_{x=\max\{1,z-6\}}^{\min\{6,z-1\}}\frac1{36} $$. Thus each term in the sum is equal to 1/36, and to evaluate the sum we have to multiply 1/36 by the number of terms. If $z$ lies between 2 and 7, then $\max\{1,z-6\} = 1$ and $\min\{6,z-1\} = z-1$. So the sum goes from $x=1$ to $x=z-1$. Hence there are $z-1$ terms in the sum, and since each term is equal to 1/36, the sum is $$\frac{z-1}{36}$$. In a similar way, you should be able to work out that if $z$ lies between 7 and 12 then the number of terms in the sum is $13-z$ and so their sum is $$\frac{13-z}{36}$$.

Thank you very much! I understand it with your explanation. =D
 
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