2. INFORMATION THEORY BAYESIAN STATISTICS I Thomas Tiahrt, MA, PhD CSC492 – Advanced Text Analytics

3. Bayesians vs. Frequentists 2  Frequentist statistics

4. Bayesians vs. Frequentists 3  Frequentist statistics  Probability is the proportion of outcomes

5. Bayesians vs. Frequentists 4  Frequentist statistics  Probability is the proportion of outcomes  Bayesian statistics

6. Bayesians vs. Frequentists 5

7. Conditional Probability Derivations 6

8. More Conditional Probability Derivation 7

9. Bayes Theorem 8

10. Marbles from Jars Example 9  Jar A  8 green marbles  2 red marbles  Jar B  3 green marbles  7 red marbles

11. Marbles from Jars Example 10

12. Marbles from Jars Example 11

13. Marbles from Jars Example 12

14. Marbles from Jars Example 13

15. References 14 Sources: Foundations of Statistical Natural Language Processing, by Christopher Manning and Hinrich Schütze The MIT Press Fundamentals of Information Theory and Coding Design, by Roberto Togneri and Christopher J.S. deSilva Chapman & Hall / CRC

16. The end of part one of Bayesian statistics has come. End of PowerPoint 15