Applying Induction to Inclusion-Exclusion Principle for Probability Measures

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SUMMARY

The discussion focuses on applying induction to the Inclusion-Exclusion Principle for probability measures. It establishes that for events \( A_i \), the probability of their intersection can be expressed as a series of sums and alternating signs involving their unions. The notation \( P \) represents a probability measure mapping events to the interval [0,1]. The key takeaway is the formula for \( P(\bigcap_{i=1}^n A_i) \) derived through induction, highlighting the relationship between intersections and unions of events.

PREREQUISITES
  • Understanding of probability measures and their properties
  • Familiarity with the Inclusion-Exclusion Principle
  • Knowledge of mathematical induction techniques
  • Basic set theory, specifically regarding unions and intersections of sets
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  • Learn about probability measures and their applications in statistics
  • Explore advanced topics in set theory related to probability
  • Practice mathematical induction with various examples and proofs
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Mathematicians, statisticians, and students studying probability theory who wish to deepen their understanding of the Inclusion-Exclusion Principle and its applications in probability measures.

Julio1
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$P(\displaystyle\bigcap_{i=1}^n A_i)=\displaystyle\sum_{i=1}^n P(A_i)-\displaystyle\sum_{i<j} P(A_i\cup A_j)+\displaystyle\sum_{i<j<k} P(A_i\cup A_j\cup A_k)-\cdots - (-1)^n P(A_1\cup A_2\cup ... \cup A_n).$

Hello, the Hint is use induction on $n$.
 
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I presume that the "A_i" are sets but what is "P"?
 
HallsofIvy said:
I presume that the "A_i" are sets but what is "P"?

Hello HallsofIvy. It is understood that $A_i$ are events and $P$ is a measure of probability, i.e.:

$P: \mathcal{A}\to [0,1], A\mapsto P(A).$
 

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