Successfully reported this slideshow.
We use your LinkedIn profile and activity data to personalize ads and to show you more relevant ads. You can change your ad preferences anytime.
Upcoming SlideShare
×

# Bayesian statistical concepts

12,476 views

Published on

My talk from November 5th, 2015 in Bielefeld, Germany. Invited by JP de Ruiter and presented to the CITEC group.

• Full Name
Comment goes here.

Are you sure you want to Yes No
Your message goes here
• Follow the link, new dating source: ❶❶❶ http://bit.ly/2Q98JRS ❶❶❶

Are you sure you want to  Yes  No
Your message goes here
• Sex in your area is here: ♥♥♥ http://bit.ly/2Q98JRS ♥♥♥

Are you sure you want to  Yes  No
Your message goes here
• How is it possible? 80% accuracy? Sports picks directly from the insiders?  http://ishbv.com/zcodesys/pdf

Are you sure you want to  Yes  No
Your message goes here
• Hey guys! Who wants to chat with me? More photos with me here 👉 http://www.bit.ly/katekoxx

Are you sure you want to  Yes  No
Your message goes here
• @GerardRidgway Sure. After each proposal of the game (slide 6, 10, 13) I had a member of the audience come up and actually play. Then after playing the could guess which bag they had, and I offered them an even bet (1 euro if they win, pay me 1 euro if they lose). For these bets, the best choice is to bet on the bag with higher posterior odds. But if I offered lopsided bets, say win 5 euro if right or pay me 1 euro if wrong, then the magnitude of the posterior odds has to enter the equation (just just asking which it favors)

Are you sure you want to  Yes  No
Your message goes here

### Bayesian statistical concepts

1. 1. BAYESIAN STATISTICAL CONCEPTS A gentle introduction Alex Etz @alxetz ßTwitter (no ‘e’ in alex) alexanderetz.com ßBlog November 5th 2015
2. 2. Why do we do statistics? •  Deal with uncertainty •  Will it rain today? How much? •  When will my train arrive? •  Describe phenomena •  It rained 4cm today •  My arrived between at 1605 •  Make predictions •  It will rain between 3-8 cm today •  My train will arrive between 1600 and 1615
3. 3. Prediction is key •  Description is boring •  Description: •  On this IQ test, these women averaged 3 points higher than these men •  Prediction is interesting •  Prediction: •  On this IQ test, the average woman will score above the average man •  Quantitative (precise) prediction is gold •  Quantitative prediction: •  On this IQ test, women will score between 1-3 pts higher than men
4. 4. Evidence is prediction •  Not just prediction in isolation •  Competing prediction •  Statistical evidence is comparative
5. 5. Candy bags 5 orange, 5 blue 10 blue
6. 6. Candy bags •  I propose a game •  Draw a candy from one of the bags •  You guess which one it came from •  After each draw (up to 6) you can bet (if you want)
7. 7. Candy bags •  If orange •  Bag A predicts orange with probability .5 •  Bag B predicts orange with probability 0 •  Given orange, there is evidence for A over B •  How much? •  Infinity •  Why? •  Outcome is impossible for bag B, yet happened •  Therefore, it cannot be bag B
8. 8. Candy bags •  If blue •  Bag A predicts blue with probability .5 (5 out of 10) •  Bag B predicts blue with probability 1.0 (10 out of 10) •  Cannot rule out either bag •  Given blue, there is evidence for B over A •  How much? •  Ratio of their predictions •  1.0 divided by .5 = 2 per draw
9. 9. Evidence is prediction •  There is evidence for A over B if: •  Prob. of observations given by A exceeds that given by B •  Strength of the evidence for A over B: •  The ratio of the probabilities (very simple!) •  This is true for all of Bayesian statistics •  More complicated math, but same basic idea •  This is not true of classical statistics
10. 10. Candy bag and a deck of cards •  Same game, 1 extra step •  I draw one card from a deck •  Red suit (Heart, Diamond) I draw from bag A •  Black suit (Spade, Club) I draw from bag B •  Based on the card, draw a candy from one of the bags •  You guess which one it came from •  After each draw (up to 6) you can bet •  (if you want)
11. 11. Candy bags and a deck of cards •  If orange, it came from bag A 100%. Game ends •  If blue •  Both bags had 50% chance of being selected •  Bag A predicts blue with probability .5 (5 out of 10) •  Bag B predicts blue with probability 1.0 (10 out of 10) •  Evidence for B over A •  How much? •  Ratio of their predictions •  1.0 divided by .5 = 2 per blue draw
12. 12. Candy bags and a deck of cards •  Did I add any information by drawing a card? •  Did it affect your bet at all? •  If the prior information doesn’t affect your conclusion, it adds no information to the evidence •  “Non-informative”
13. 13. Candy bags and a deck of cards •  Same game, 1 extra step •  I draw one card from a deck •  King of hearts I draw from bag B •  Any other card I draw from bag A •  I draw a ball from one of the bags •  You guess which one it came from •  After each draw (up to 6) you can bet
14. 14. Candy bags and a deck of cards •  Did I add any information by drawing a card? •  Did it affect your bet at all? •  Observations (evidence) the same •  But conclusions can differ •  Evidence is separate from conclusions
15. 15. • The 1 euro bet •  If orange draw •  Bet on bag A, you win 100% •  We have ruled out bag B •  If blue draw •  Bet on bag A, chance you win is x% •  Bet on bag B, chance you win is (1-x)% Betting on the odds
16. 16. Betting on the odds •  Depends on: •  Evidence from sample (candies drawn) •  Other information (card drawn, etc.) •  A study only provides the evidence contained in the sample •  You must provide the outside information •  Is the hypothesis initially implausible? •  Is this surprising? Expected?
17. 17. Betting on the odds •  If initially fair odds •  (Draw red suit vs. black suit) •  Same as adding no information •  Conclusion based only on evidence •  For 1 blue draw •  Initial (prior) odds 1 to 1 •  Evidence 2 to 1 in favor of bag B •  Final (posterior) odds 2 to 1 in favor of bag B •  Probability of bag B = 67%
18. 18. Betting on the odds •  If initially fair odds •  (Draw red suit vs. black suit) •  Same as adding no information •  Conclusion based only on evidence •  For 6 blue draws •  Initial (prior) odds 1 to 1 •  Evidence 64 to 1 in favor of bag B •  Final (posterior) odds 64 to 1 in favor of bag B •  Probability of bag B = 98%
19. 19. Betting on the odds •  If initially unfair odds •  (Draw King of Hearts vs. any other card) •  Adding relevant outside information •  Conclusion based on evidence combined with outside information •  For 1 blue draw •  Initial (prior) odds 1 to 51 in favor of bag A •  Evidence 2 to 1 in favor of bag B •  Final (posterior) odds 1 to 26 in favor of bag A •  Probability of bag B = 4%
20. 20. Betting on the odds •  If initially unfair odds •  (Draw King of Hearts vs. any other card) •  Adding relevant outside information •  Conclusion based on evidence combined with outside information •  For 6 blue draws •  Initial (prior) odds 1 to 51 in favor of bag A •  Evidence 64 to 1 in favor of bag B •  Final (posterior) odds 1.3 to 1 in favor of bag B •  Probability of bag B = 55%
21. 21. Betting on the odds •  The evidence was the same •  2 to 1 in favor of B (1 blue draw) •  64 to 1 in favor of B (6 blue draws) •  Outside information changed conclusion •  Fair initial odds •  Initial prob. of bag B = 50% •  Final prob. of bag B = 67% (98%) •  Unfair initial odds •  Initial prob. of bag B = 2% •  Final prob. of bag B = 4% (55%)
22. 22. Should you take the bet? •  If I offer you a 1 euro bet: •  Bet on the bag that has the highest probability •  For other bets, decide based on final odds
23. 23. Evidence is comparative • What if I had many more candy bag options?
24. 24. Graphing the evidence •  What if I wanted to compare every possible option at once? •  Graph it!
25. 25. Graphing the evidence (1 blue)
26. 26. Graphing the evidence (1 blue)
27. 27. Graphing the evidence (6 blue)
28. 28. Graphing the evidence (6 blue)
29. 29. Graphing the evidence •  This is called a Likelihood function •  Ranks probability of the observations for all possible candy bag proportions •  Evidence is the ratio of heights on the curve •  A above B, evidence for A over B
30. 30. Graphing the evidence •  Where does prior information enter? •  Prior rankings for each possibility •  Just as it did before •  But now as a prior distribution
31. 31. Prior information •  “Non-informative” prior information •  All possibilities ranked equally •  i.e. no value preferred over another •  Weak prior information; vague knowledge •  “The bag has some blue candy, but not all blue candy” •  After Halloween, for example •  Saw some blue candy given out, but also other candies •  Strong prior information •  “Proportion of women in the population is between 40% and 60%”
32. 32. Non-informative No preference for any values
33. 33. Weakly-informative Some blue candy, but not all
34. 34. Strongly-informative Only middle values have any weight
35. 35. Information and context •  Your prior information depends on context! •  And depends on what you know! •  Just like drawing cards in the game •  Just harder to specify •  Intuitive, personal •  Conclusions must take context into account