Pure mathematician needs help with probability

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Discussion Overview

The discussion revolves around calculating the probability that for two random variables X1 and X2, which can take values from a finite set, the condition X1 <= a <= X1 + X2 holds. Participants explore various methods to derive this probability and its implications, including the use of probability mass functions (pmf) and cumulative distribution functions (cdf).

Discussion Character

  • Technical explanation
  • Mathematical reasoning
  • Exploratory

Main Points Raised

  • One participant describes the pmf for X1 and Y = X1 + X2, noting that both random variables have equal probabilities for their defined values.
  • Another participant suggests that the probability for Y can be computed as a convolution of X1 with itself, leading to a summation over possible values.
  • A different approach is proposed involving a 2D grid representation of the random variables to visualize the region defined by the inequality X1 <= a <= X1 + X2.
  • Participants discuss the independence of events and the need to condition probabilities based on the defined events.
  • There is mention of calculating the probability of Y being less than a and how that relates to the overall probability of the defined conditions.

Areas of Agreement / Disagreement

Participants express various methods and approaches to tackle the problem, but no consensus is reached on a definitive solution or method. Multiple competing views and techniques remain present in the discussion.

Contextual Notes

Participants acknowledge limitations in their understanding of probability and the complexity of the problem, which may affect their proposed solutions.

gccdman
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Hi,

I've posted this on other forums and have had little to no help. I thought this forum looked very good so hoping someone can help.

Sorry for the non specific title of the thread, I don't know what the following problem should be called! I don't think it is too hard but I stopped studying probability after my first year at uni and do not have much intuition in this field of mathematics. Anyway, here goes...

If X1 and X2 are random variables that can take any values in the finite set {0,2,4,...,2n} all with equal chance then how do I work out the probability, given an arbitrary positive real a that:

X1 <= a <= X1+X2

If we call this probability P(a), how do we find a such that

P(a) => P(b) for arbitrary positive real b


Acting on advice I have calculated the pmf for X1 and Y= X1 + X2

X1: S -> R is defined by th identity function X1(s) = s.

Hence, f1(x) = P(X1 = x) = P({s € S: X1(s) = x}) = P({s€S: s = x) = 0 if x does not belong to {0,2,...,2n}; 1/(n+1) if x € S

Since X2: S -> R is defined by X2(s) = s we have f1(x) = f2(x).

Let Y = X1 + X2. Y: SxS -> R is defined by Y(s,s') = X1(s) + X2(s') = s + s'.

Hence fY(x) = P(Y = x) = P({(s,s') € SxS : Y(s,s') = x}) = P({(s,s') € SxS: s + s' = x) = 0 if x does not belong to {0,2,...,4n}; (x+2)/2(n+1)2 if x € {0,2,...,2n}; (2(2n+1)-x)/2(n+1)2 if x € {2n, 2n+2,..., 4n}

However, after this no further advice followed.

In the process of trying to solve it my self and guessing what the further advice may have been I have calculated explicitly the cdf for X1 and Y = X1 + X2 which I shall denote FX1(x) and FY(x) respectively.

Let E be the event X1 <= x, E' the event x <= Y.

Now if E and E' were independent by definition P(E and E') = P(E)P(E') = FX1(x)(1-FY(x)+fY(x)) and hence the problem would be solved. However, E' is conditional on E. I would go on to solve this problem by working out fY/E(x) and FY/E(x),

for then P(E and E') = P(E)P(E'/E) = FX1(x)(1-FY/E(x)+fY/E(x)).

Is this the right way of solving the problem? Is there a way of calculating fY/E(x) and FY/E(x) from fX1(x), FX1(x), fY(x) and FY(x)?

Any advice would be much appeciated,

Thanks
 
Last edited:
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hi, not exactly an explicit expression & my probability's a little sketchy, (enough disclaimers) this is what I came up with

first the probability for the single random variable is:

[tex]P(X=m) = \frac{1}{(n+1)}[/tex]
for m in {0,2,...2n}, 0 otherwise

the probability of Y = X1 + X2 will then be a convolution of X with itself, where the sum is over all values of r
[tex]P(Y=(X_1+X_2)=m) = \sum_r P(X_1=r)P(X_2=m-r)[/tex]
which after summing, hopefully looks something like what you got...

Then the probability of Y< a is:
[tex]P(Y<a) = \sum_m^{a-1} \sum_r P(X_1=r)P(X_2=m-r)[/tex]
note the "a" used in the sum actually represents the integer part of a...

And the probability of Y>= a is:
[tex]P(Y>=a) = 1 - P(Y<a) = 1 - \sum_m^{a-1} \sum_r P(X_1=r)P(X_2=m-r)[/tex]

Now we want the proabibility AND case when X1 <=a and Y>=a, so looking at the probabily for X1<= a
[tex]P(X<=a) = \sum_r^a P(X_1=r)[/tex]

Now looking at the expresssion for Y>= a, the X1 cases appear explicitly, so confining the sum to only those cases when X1<=a in the expression should represent the AND case:
[tex](P(Y=X_1+X_2>=a)) \wedge (P(X<=a)) = 1 - \sum_m^{a-1} \sum_r^a P(X_1=r)P(X_2=m-r)[/tex]

what do you think?
 
Last edited:
gccdman said:
how do I work out the probability, given an arbitrary positive real a that:

X1 <= a <= X1+X2

Another approach is to draw the 2D grid (X1,X2) and highlight the region defined by the above inequality.
 
nice one... hopefully the sum I gave should in effect count the discrete points within that region divided by (n+1)^2
 

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