Base rate probability application question

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SUMMARY

The discussion centers on the application of Bayes' theorem as illustrated in Daniel Kahneman's "Thinking, Fast and Slow." Specifically, it explains how to calculate the probability of an event (Tom W being a computer scientist) given a base rate of 3% for graduate students in computer science and a likelihood ratio of 4:1. This results in a posterior probability of 11% for Tom W being a computer scientist. The discussion emphasizes the importance of understanding the relationship between the two binary events: the belief of someone being a student and the actual event of being a student.

PREREQUISITES
  • Understanding of Bayes' theorem and its components
  • Familiarity with probability notation (e.g., P(A|B), P(B|A))
  • Basic knowledge of binary variables in probability
  • Awareness of the concept of base rates in statistical reasoning
NEXT STEPS
  • Study the application of Bayes' theorem in real-world scenarios
  • Explore the concept of likelihood ratios and their significance in probability
  • Learn about common misconceptions in interpreting base rates
  • Review examples of probability calculations involving binary events
USEFUL FOR

Students of statistics, data scientists, and anyone interested in applying Bayesian reasoning to real-world problems will benefit from this discussion.

SELFMADE
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Dear Probability subforum,

I have hard time thinking through how 11% is calculated. Anyone have any ideas? From "Thinking fast and slow" Kahneman. p 154

"...If you believe 3% of graduate students are enrolled in computer science (the base rate), and you also believe that the description of Tom W is 4 times more likely for a graduate student in that field than in other fields, then Bayes's rule says you must believe that the probability that Tom W is a computer scientist is now 11%. If the base rate had been 80%, the new degree of belief would be 94.1%. And so on..."

Description of Tom W is that of typical CS student.
 
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Hey SELFMADE.

So in this problem you have two events: the belief of someone being a student and the event that they actually are a student. We call these events A and B and they are both binary variables (i.e. true or false).

So you have the probabilities P(A|B) and P(B|A) and what you want to do is given P(A|B), you want to find the reverse of P(B|A).

Since there are many examples on the internet of this, I'll just post a link to the first one google showed that is relevant:

http://www.johndcook.com/rarediseases.pdf
 

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