Bayes rule and joint probability

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SUMMARY

The discussion centers on the application of Bayes' rule and joint probability, specifically the equation p(R|q,x)/p(NR|q,x)=[p(R|q) / p(NR|q)] * [p(x|R,q) / p(x|NR,q)]. Participants express confusion regarding the symbols used in the equation and the relevance of the variable 'q'. The need for clarity in defining random variables is emphasized, as it is crucial for understanding the relationship between the probabilities involved.

PREREQUISITES
  • Understanding of Bayes' theorem
  • Familiarity with joint probability concepts
  • Knowledge of conditional probability
  • Ability to interpret mathematical notation in probability
NEXT STEPS
  • Study the derivation of Bayes' theorem in detail
  • Explore joint probability distributions and their applications
  • Learn about conditional independence in probability theory
  • Review examples of Bayes' rule applied in real-world scenarios
USEFUL FOR

Students of statistics, data scientists, and anyone interested in understanding probabilistic models and their applications in decision-making processes.

elr0d
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I try to understand the following equation but I can not.
I have basic knowledge about Bayes rule and joint probability.
How can we produce this result? I would appreciate any help.

p(R|q,x)/p(NR|q,x)=[p(R|q) / p(NR|q) ] * [p(x|R,q) / p(x|NR,q)]
 
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I have some trouble with your equation, since you haven't defined the symbols. Which are random variables, etc.? It looks to me q is irrelevant.
 

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