Probability of a curve in joint density plane

In summary, the probability of two continuous random variables being equal is typically zero, but there are cases where it may not be true due to dependency between the variables.
  • #1
CantorSet
44
0
Suppose X and Y are continuous random variables with the joint pdf [tex]f_{xy}(x,y)[/tex] on the [tex][0,1] \times [0,1][/tex] square.

Is the probability [tex]P(X = Y)[/tex] then equal to zero since probability here is a volume, and the set that satisfies [tex]P(X = Y)[/tex] is a plane?

Supposing it's not zero, when I tried to evaluate it with the integral

[tex] \int_{0}^{1} f_{xy}(t,t)\sqrt2dt[/tex]

(basically, just a line integral) the answer seems way too big.

Any thoughts from the folks out there?
 
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  • #2
Whenever random variables are continuous the probability of their equality is zero.
 
  • #3
thanks.
 
  • #4
statdad said:
Whenever random variables are continuous the probability of their equality is zero.
This is not always true. If there is some dependency between X and Y, it may not be true. An extreme example is X=Y, so P(X=Y)=1.
 
  • #5
mathman said:
This is not always true. If there is some dependency between X and Y, it may not be true. An extreme example is X=Y, so P(X=Y)=1.

Hmmm. I would argue that if X=Y then there is only one random variable. (As stated I'm looking at your comment as a different situation than the case where we construct independent - identically distributed copies of random variables in probability theory.)
 
  • #6
statdad said:
Hmmm. I would argue that if X=Y then there is only one random variable. (As stated I'm looking at your comment as a different situation than the case where we construct independent - identically distributed copies of random variables in probability theory.)

Fussy! Another example: X=max(Y,1/2). Then P(X=Y)=P(Y≥1/2). You can make up any more as you wish. The main point is the obvious dependency between X and Y.
 

1. What is the joint density plane?

The joint density plane is a two-dimensional graph that represents the probability distribution of two variables. It is used to visualize the relationship between two variables and their probability of occurrence.

2. How is the probability of a curve calculated in the joint density plane?

The probability of a curve in the joint density plane is calculated by finding the area under the curve using calculus. This area represents the likelihood of the two variables occurring together at a specific point on the graph.

3. What does the shape of the curve in the joint density plane indicate?

The shape of the curve in the joint density plane indicates the type of relationship between the two variables. A bell-shaped curve indicates a normal distribution, while a skewed curve indicates a non-normal distribution.

4. How does the probability of a curve change as the variables change?

The probability of a curve in the joint density plane changes as the variables change. As the variables become more correlated, the curve becomes narrower and taller, indicating a higher probability of occurrence.

5. What is the importance of understanding the probability of a curve in the joint density plane?

Understanding the probability of a curve in the joint density plane is important in many fields such as statistics, economics, and engineering. It allows for the analysis of relationships between variables and can help in making predictions and decisions based on probability.

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