Independence of events question

In summary, The formula states that if a sequence of events is independent, then a partition of the sequence will also result in an independent sequence of events. To prove this, one must show that the probability of the intersection of all the events in the sequence is equal to the product of the probabilities of each individual event. This can be done using the inclusion-exclusion principle and the independence of the events.
  • #1
geoffrey159
535
72
Hello,

I am studying independence of events and I came across a formula that I don't understand. It is rather technical, not very interesting, but I feel stuck and it stays in my mind. Could you explain the following :

If ## (A_i)_{i\in I}## is a sequence of independent events on ##(\Omega,{\cal A}, P)##, then, given the partition ## I_1 \cup I_2## of ##I##, the sequence of events ##(B_i)_{i\in I}## defined by ## B_i = A_i ## if ##i\in I_1## and ##B_i = A_i^c## if ##i\in I_2## is also independent.

To prove this, one must show that for any finite set ##J\subset I## : ##P(\bigcap_{j\in J} B_j) = \prod_{j\in J} P(B_j)##

Since ## J = J_1 \cup J_2##, where ## J_i = I_i \cap J ##, we have ## \bigcap_{j\in J} B_j = (\bigcap_{j\in J_1} A_j) \bigcap (\bigcap_{j\in J_2} A_j^c) =(\bigcap_{j\in J_1} A_j) - (\bigcup_{j\in J_2} A_j) ##

so that
##P(\bigcap_{j\in J} B_j) = P(\bigcap_{j\in J_1} A_j) - P(\bigcup_{j\in J_2} A_j) ##

With the inclusion-exclusion principle
## P(\bigcup_{j\in J_2} A_j) = \sum_{ K\subset J_2 \ |K| = 1} P(\bigcap_{j\in K} A_j ) - \sum_{ K\subset J_2 \ |K| = 2} P(\bigcap_{j\in K} A_j ) + ... + (-1)^{|J_2|-1} P(\bigcap_{j\in J_2} A_j) ##

But I don't see how to finish this once ##P(\bigcap_j A_j ) ## is developped into a product !
 
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  • #2
Oh I've made a mistake I get it now :

##\bigcap_{j\in J} B_j = A\cap B^c = A - A\cap B##

with ##A = \bigcap_{j\in J_1} A_j## and ##B = \bigcup_{j\in J_2} A_j ##

we have ## A \cap B = \bigcup_{j\in J_2} \bigcap_{k\in J_1} (A_k \cap A_j) ##

And with the principle of inclusion exclusion and the independence of ##(A_i)_{i\in I}## we find ## P( A \cap B) = P(A) P(B) ##
 
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1. What is the meaning of independence of events?

The independence of events is a statistical concept that refers to the relationship between two or more events. If two events are independent, the occurrence of one event does not affect the probability of the other event happening.

2. How can I determine if two events are independent?

To determine if two events are independent, you can use the mathematical formula P(A and B) = P(A) * P(B). If the probability of both events happening together is equal to the product of their individual probabilities, then the events are considered independent.

3. What is the difference between independent and dependent events?

Independent events are those in which the occurrence of one event does not affect the probability of the other event happening. Dependent events, on the other hand, are events in which the occurrence of one event does affect the probability of the other event happening.

4. Can two events be both independent and dependent?

No, two events cannot be both independent and dependent. The concept of independence and dependence are mutually exclusive. A pair of events can either be independent or dependent, but not both.

5. How does the independence of events affect probability calculations?

The independence of events affects probability calculations by allowing us to use the multiplication rule for independent events. This rule states that the probability of two independent events occurring together is equal to the product of their individual probabilities. This allows for simpler and more accurate probability calculations.

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