How to calculate this probability (conditional distributions)

In summary, the conversation is discussing the calculation of the probability of the event (x > 0.5) and (y < 0.5) based on a given density function. The speaker is unsure of the answer due to not marking the axes in the figures provided. However, if the density function is 0 for x > y, then the probability of the event is also 0.
  • #1
Drao92
72
0
fXY(x,y)=2 if 0<x<1 and x<y<1, 0 for other intervals
I have to calculate: P((x>0.5)π(y<0.5)).
I think it 0 but I am not sure because in all other exercises I've made the surfaces intersect each other. Like in fig 1 for P((x<0.5))π(y<0.5))=integral from 0 to 0.5 from integral from x to 0.5 from 2dydx.
The fig 2 is for P((x>0.5)π(y<0.5)).
Im sorry because i forgot to mark the axis. vertical is y and horizontal is x.I apologize.
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  • #2
If I understand your description, the density function = 0 for x > y. Therefore P((x > 0.5) and (y < 0.5)) = 0.
 

1. What is a conditional distribution?

A conditional distribution is a type of probability distribution that represents the probability of an event occurring given that another event has already occurred. It is used to model situations where the outcome of an event depends on the outcome of another event.

2. How do you calculate a conditional distribution?

To calculate a conditional distribution, you need to first determine the joint probability of the two events, which is the probability of both events occurring. Then, you divide this joint probability by the probability of the event that has already occurred. This will give you the conditional probability of the second event given the first event.

3. What is the difference between a conditional distribution and a marginal distribution?

A conditional distribution is a distribution that takes into account the occurrence of a specific event, while a marginal distribution represents the overall probability of an event occurring without considering any other events. In other words, a conditional distribution is a subset of a marginal distribution.

4. When should I use a conditional distribution?

A conditional distribution is useful when you want to model the probability of an event occurring given that another event has already occurred. It is commonly used in statistics, machine learning, and other fields to analyze and make predictions about data.

5. Can a conditional distribution be used to make predictions?

Yes, a conditional distribution can be used to make predictions about the likelihood of an event occurring given that another event has already occurred. This is often done in machine learning and data analysis to make informed decisions based on past data.

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