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1.10 Bayes' Theorem

Overview:

Bayes' Theorem connects prior probabilities (initial beliefs) with posterior probabilities (updated beliefs) based on new evidence. It is a cornerstone of conditional probability.

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  • \( P(A|B) \): Posterior probability of event A, given that event B has occurred.
  • \( P(A) \): Prior probability of event A.
  • \( P(B|A) \): Probability of event B occurring, given that event A has occurred.
  • \( P(B) \): Total probability of event B.

Application Example:

Scenario: Two movies—KGF (Event A) and Kantara (Event B).

  • \( P(A) \): Probability of KGF being a hit = 0.34.
  • \( P(B) \): Probability of Kantara being a hit = 0.60.
  • \( P(A ∩ B) \): Probability of both being hits = 0.18.

Calculate \( P(A|B) \) (KGF being a hit, given Kantara is a hit):

image

  • Interpretation: Knowing Kantara is a hit reduces the probability of KGF being a hit from 0.34 to 0.30.

Calculate \( P(B|A) \) (Kantara being a hit, given KGF is a hit):

image

  • Interpretation: Knowing KGF is a hit reduces the probability of Kantara being a hit from 0.60 to 0.53.

Insights from the Example:

  1. Dependency: The events (KGF and Kantara being hits) are not independent, as the joint probability \( P(A ∩ B) \) does not equal \( P(A) x P(B) \).
  2. Posterior vs. Prior: Posterior probabilities (\( P(A|B), P(B|A) \)) differ from prior probabilities (\( P(A), P(B) \)) due to event interdependence.

Generalization (Law of Total Probability):

Bayes' Theorem can be extended using the Law of Total Probability: image

  • Mutually Exclusive and Collectively Exhaustive Events image

  • Example: For Kantara revenue levels (4L, 5L, 6L):

    • \( P(A) \): Probability of KGF being a hit.
    • Break down \( P(A) \) into contributions from all revenue levels of Kantara.
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