INFORMATION THEORY BAYESIAN STATISTICS II Thomas Tiahrt, MA, PhD CSC492 – Advanced Text Analytics.
14
INFORMATION THEORY BAYESIAN STATISTICS II Thomas Tiahrt, MA, PhD CSC492 – Advanced Text Analytics
-
Upload
dennis-phelps -
Category
Documents
-
view
218 -
download
2
Transcript of INFORMATION THEORY BAYESIAN STATISTICS II Thomas Tiahrt, MA, PhD CSC492 – Advanced Text Analytics.
- Slide 1
- Slide 2
- INFORMATION THEORY BAYESIAN STATISTICS II Thomas Tiahrt, MA, PhD CSC492 Advanced Text Analytics
- Slide 3
- Extended Form of Bayes Theorem 2
- Slide 4
- Extended Form Bayes Theorem 3
- Slide 5
- 4
- Slide 6
- Medical Tests 5 False positive Test falsely indicates patient has disease False negative Test falsely indicates patient does not have disease
- Slide 7
- Medical Test Details 6 Disease tested for afflicts:5 of 1,000 When test returns a positive: Rate of false positives:3% Patient has disease :100%-3%=97% When test returns a negative: Rate of false negatives: 1% Patient does not have disease: 100%-1%=99%
- Slide 8
- Medical Test Details 7 Disease tested for afflicts5 of 1,000 When test returns a positive: Rate of false positives:3% Patient has disease :100%-3%=97% When test returns a negative: Rate of false negatives: 1% Patient does not have disease: 100%-1%=99%
- Slide 9
- Doctors Questions 8 Given a positive test: What is the probability that a randomly chosen person actually has the disease? Given a negative test: What is the probability that a randomly chosen person does not have the disease?
- Slide 10
- Conditional Probabilities 9
- Slide 11
- Prevalence in Population 10
- Slide 12
- Bayes Theorem in Action 11
- Slide 13
- Bayes Theorem in Action 12
- Slide 14
- References 13 Sources: Foundations of Statistical Natural Language Processing, by Christopher Manning and Hinrich Schtze The MIT Press Fundamentals of Information Theory and Coding Design, by Roberto Togneri and Christopher J.S. deSilva Chapman & Hall / CRC
- Slide 15
- The end of part two of Bayesian statistics has come. End of PowerPoint 14