Probability of Independent Sets: A,B in Sample Space Z

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In summary, the conversation discusses the concept of independence in probability, specifically in relation to sets and outcomes. The question at hand is how to determine the values of x and y in a sample space Z = {a, b, c} in order for the sets {a, b} and {a, c} to be considered independent. The conversation also touches on the definition of independence in this context and how it applies to the given sets.
  • #1
ParisSpart
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In a sample space Z = {a, b, c} is the probability distribution of the numbers x, y, 0.3 (in that order). What should be they that any {a, b} and {a, c} are independent?

maybe i need a suggestion if the type i want to use down is correcty or not because i stucked...



I know that for A1,A2,An we have this type : P(A1andA2and...andAn)=P(A1)P(A2)...P(An) but how i can use this if i have sets?
 
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  • #2
You need a to be independent of b and c, but b and c need not be independent of each other?

What is the definition of "independent" in this context? (Hint: in terms of probabilities.)
You have:
P(A1andA2and...andAn)=P(A1)P(A2)...P(An) but how i can use this if i have sets?
... you don't have to apply the relation to whole sets, the A1,A2,... form a set {A1, A2, ...}.

if A and B are independent, then P(A)+P(B)=?
 
  • #3
If I'm interpreting this correctly, you need to find x and y such that P[f∈{a,b}|f∈{a,c}] = P[f∈{a,b}]. Is that it?
 
  • #4
Well if you are interpreting it correctly - then how would the relation P[f∈{a,b}|f∈{a,c}] = P[f∈{a,b}] possibly hold? After all, the two sets have a member in common.

It boils down to how you read this bit:
What should be they that any {a, b} and {a, c} are independent?
... which is "they" and what is "independent" of what?

Presumably "they" is x and y.
"{a,b} is independent", then I'd read that as outcome a is independent of outcome b... I don't think that {a,b} can be independent of {a,c} since they both contain a.

I read the question as saying that {a,b,c} may or may not not be independent, but {a,b} and {a,c} are.
Given that P(c)=0.3, what is P(a) and P(b)?

However: you are closer to the course than I am so you may have an extra insight.
 
  • #5
Simon Bridge said:
Well if you are interpreting it correctly - then how would the relation P[f∈{a,b}|f∈{a,c}] = P[f∈{a,b}] possibly hold? After all, the two sets have a member in common.
There is exactly one setting of x and y that makes it true.
"{a,b} is independent", then I'd read that as outcome a is independent of outcome b.
Aren't a, b and c atomic events? If so, they are therefore mutually exclusive, not independent. In probability space terms, a set of possible outcomes, like {a, b} constitutes a (nonatomic) event, and you want the events {a, b}, {a, c} to be independent.
 
  • #6
Because Z is a sample space? That would make sense OK.
I still think the phrasing is sloppy... which is why I'm uncertain about how I was reading the question.
 

1. What is the definition of "Probability of Independent Sets?"

The probability of independent sets refers to the likelihood of two or more events occurring simultaneously within a given sample space. This means that the outcome of one event does not affect the outcome of the other event. It is represented by the formula P(A and B) = P(A) * P(B), where A and B are two independent events within the sample space Z.

2. How is the probability of independent sets calculated?

The probability of independent sets is calculated using the formula P(A and B) = P(A) * P(B). The probability of event A is multiplied by the probability of event B to determine the likelihood of both events occurring together. This formula only applies to independent events, where the outcome of one event does not affect the outcome of the other.

3. Can the probability of independent sets be greater than 1?

No, the probability of independent sets cannot be greater than 1. The probability of an event occurring can range from 0 (impossible) to 1 (certain), and the multiplication of two probabilities will always result in a number between 0 and 1. If the calculated probability is greater than 1, it is likely that there is an error in the calculation.

4. What is the significance of independent events in probability?

Independent events are significant in probability because they allow us to make more accurate predictions and calculations. When events are independent, the probability of one event occurring does not affect the probability of the other event occurring. This allows us to use the simple multiplication formula to calculate the probability of both events happening together, making probability calculations more efficient.

5. How can the concept of independent events be applied in real-life situations?

The concept of independent events can be applied in various real-life situations, such as in gambling and insurance. In gambling, the outcome of one game does not affect the outcome of another game, making each bet an independent event. In insurance, the likelihood of multiple events, such as car accidents or natural disasters, occurring at the same time can be calculated using the probability of independent events.

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