Lecture_4 - Stat 2001 Week 3 Recap Bayes Theorem Suppose it...

Info iconThis preview shows pages 1–5. Sign up to view the full content.

View Full Document Right Arrow Icon
Stat 2001 Week 3
Background image of page 1

Info iconThis preview has intentionally blurred sections. Sign up to view the full version.

View Full Document Right Arrow Icon
Recap Bayes Theorem Suppose it is Wednesday night and I am at a party. I have told you that you will have a quiz on Thursday unless I drink too much and forget. So you call my friend and ask her “Do you think Priya has drunk too much tonight?” Based on her experience with me at parties, her prior probability of me drinking too much is 25% . But you wonder if my drinking habits change in the presence of other factors, so you keep pestering my friend for information and find out that when I drink too much at a party, the conditional probability of me behaving badly is 40% whereas when I don’t drink too much the conditional probability of me behaving badly is only 10% . What is the posterior probability of me having drunk too much given that I am observed behaving badly?
Background image of page 2
Background image of page 3

Info iconThis preview has intentionally blurred sections. Sign up to view the full version.

View Full Document Right Arrow Icon
Another application of Bayes Rule About 2% of people seeking asylum in Australia arrive by boat, the remaining 98% arriving by some other means. Of those who arrive by boat, 90% are detected and detained,
Background image of page 4
Image of page 5
This is the end of the preview. Sign up to access the rest of the document.

{[ snackBarMessage ]}

Page1 / 10

Lecture_4 - Stat 2001 Week 3 Recap Bayes Theorem Suppose it...

This preview shows document pages 1 - 5. Sign up to view the full document.

View Full Document Right Arrow Icon
Ask a homework question - tutors are online