Calculating Joint Conditional Probability with Independent Variables

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SUMMARY

The discussion focuses on calculating joint conditional probability using independent variables. The user seeks to determine P(A|x,y) given P(x|A), P(y|A), P(x), P(y), and P(A). The correct formula derived is P(A|x,y) = P(x,y|A) * P(A) / (P(x) * P(y)), confirming that the independence of x and y simplifies the calculation. The user expresses uncertainty about their understanding of probability concepts.

PREREQUISITES
  • Understanding of conditional probability
  • Knowledge of Bayes' theorem
  • Familiarity with independent events in probability
  • Basic probability notation and terminology
NEXT STEPS
  • Study Bayes' theorem in depth
  • Learn about independent and dependent events in probability
  • Explore joint probability distributions
  • Practice problems involving conditional probabilities with independent variables
USEFUL FOR

Students studying probability theory, data scientists working with probabilistic models, and anyone interested in understanding joint conditional probabilities in statistics.

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Homework Statement


I have access to P(x|A) and P(y|A), P(x), P(y) and P(A), in addition to the knowledge that x and y are independent variables. I am interested in finding P(A|x,y).

The Attempt at a Solution



I think that
P(A|x,y) = P(x,y|A) * P(A) / P(x,y) = P(x,y|A) * P(A) / (P(x)*P(y))

I am not particularly good at probability and am dealing with probabilities after considerable time, would like to know if i am doing anything wrong or is my answer correct.
 
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It looks good to me.
 

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