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# Quantitative Methods for Lawyers - Class #8 - Bayes Rule and Conditional Probability - Professor Daniel Martin Katz

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Quantitative Methods for Lawyers - Class #8 - Bayes Rule and Conditional Probability - Professor Daniel Martin Katz

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### Quantitative Methods for Lawyers - Class #8 - Bayes Rule and Conditional Probability - Professor Daniel Martin Katz

1. 1. Quantitative Methods for Lawyers Conditional Probability & Bayes Theorem ! Class #8 @ computational computationallegalstudies.com professor daniel martin katz danielmartinkatz.com lexpredict.com slideshare.net/DanielKatz
2. 2. Conditional Probability is an Important Concept and A Precursor to Discussing Bayes Rule Conditional Probability Relies on a Little Bit of Set Theory” Prob of “A Given B” P (A intersect B) Divided by the Prob of B
3. 3. In a conditional probability problem, the sample space is “reduced” to the “space” of the given outcome (e.g. if given B, we now just care about the probability of A occurring “inside” of B) Given B, what’s the probability of A? A Visual Depiction of Conditional Probability The Entire Yellow Space is Intuitively we are asking ... What Share of B contains the overlapwithA?
4. 4. A Dice Based Example What is the Probability of Getting a “2” if we know that the number thrown is less than 5?
5. 5. What is the Probability of Getting a “2” if we know given that the number thrown is less than 5? A Dice Based Example
6. 6. What is the Probability of Getting a “2” if we know given that the number thrown is less than 5? Again Here is Our Formula: A Dice Based Example
7. 7. What is the Probability of Getting a “2” if we know given that the number thrown is less than 5? P ( “2”|know it is Less than 5) = P ( “2” {1,2,3,4} ) P ( {1,2,3,4} ) Again Here is Our Formula: A Dice Based Example
8. 8. What is the Probability of Getting a “2” if we know given that the number thrown is less than 5? P ( “2”|know it is Less than 5) = P ( “2” {1,2,3,4} ) P ( {1,2,3,4} ) A Dice Based Example
9. 9. Okay What is P ( “2” {1,2,3,4} ) ? What is the Probability of Getting a “2” if we know given that the number thrown is less than 5? The only element that intersects is “2” so is it the Prob of “2” which is 1/6 P ( “2”|know it is Less than 5) = P ( “2” {1,2,3,4} ) P ( {1,2,3,4} ) A Dice Based Example
10. 10. Okay What is P ( “2” {1,2,3,4} ) ? What is the Probability of Getting a “2” if we know given that the number thrown is less than 5? The only element that intersects is “2” so is it the Prob of “2” which is 1/6 P ( “2”|know it is Less than 5) = P ( “2” {1,2,3,4} ) P ( {1,2,3,4} ) Now What is P ( {1,2,3,4} ) ? 1/6 + 1/6 +1/6 + 1/6 = 4/6 A Dice Based Example
11. 11. Okay What is P ( “2” {1,2,3,4} ) ? What is the Probability of Getting a “2” if we know given that the number thrown is less than 5? The only element that intersects is “2” so is it the Prob of “2” which is 1/6 P ( “2”|know it is Less than 5) = P ( “2” {1,2,3,4} ) P ( {1,2,3,4} ) Now What is P ( {1,2,3,4} ) ? 1/6 + 1/6 +1/6 + 1/6 = 4/6 Okay Lets Put it All Together: P ( “2” {1,2,3,4} ) P ( {1,2,3,4} ) = 1/41/6 4/6 = A Dice Based Example
12. 12. Monty Hall Problem
13. 13. In “Lets Make a Deal” you are given the opportunity to select one closed door of three, behind one of which there is a prize. The other two doors hide “goats” (or some other such “non– prize”), or nothing at all. Once you have made your selection, Monty Hall will open one of the remaining doors, revealing that it does not contain the prize. Monty Hall Problem
14. 14. Assume You Picked Door #1 Monty Hall Problem Now Assume Monty Has Removed Door #2 Here is the problem: Should You Stay or Should you switch?
15. 15. Monty Hall Problem Answer is You Should Switch This is Counterintuitive Key Fact: the host always opens the door to reveal a goat (if not the properties of the problem would change)
16. 16. Tree showing the probability of every possible outcome if the player initially picks Door 1 Monty Hall Problem
17. 17. There are 100 doors to pick from in the beginning You pick one door Monty looks at the 99 others, ﬁnds the goats, and opens all but 1 Do you stick with your original door (1/100), or the other door, which was ﬁltered from 99? It’s a bit clearer now : Monty is taking a set of 99 choices and improving them by removing 98 goats. When he’s done, he has the top door out of 99 for you to pick. Your decision: Do you want a random door out of 100 (initial guess) or the best door out of 99? Said another way, do you want 1 random chance or the best of 99 random chances? We’re starting to see why Monty’s actions help us. He’s letting us choose between a generic, random choice and a curated, ﬁltered choice. Filtered is better. Monty Hall Problem
18. 18. Bayes Rule
19. 19. Bayes Rule In Spam Filtering
20. 20. Spam Filtering Fighting spam is a constant exercise. As the junk ﬁlters become more intelligent, the spam senders come up with innovative means to ensure their emails reach your inbox. The automatic identiﬁcation of spam and phishing scams is usually coupled with a “human” element. This Human Element has to be Weighted / Blended.
21. 21. Spam Filtering When many people mark an email message as spam, the ﬁlter will eventually “update” (using an updating rule) The properties of a spam message are constantly in ﬂux. Thus, spam ﬁlters need to be taught constantly.
22. 22. Spam Filtering Key Insight is that when developing a ﬁlter we are trying to mimic the information that allows you (as a human reasoner) to rapidly detect that a message is spam: (1) Message is from another country ( in particular china, Nigeria, India, etc.) (2) Message is from new email address (3) ... What Else?
23. 23. Bayes Rule In Spam Filtering Some of the same properties at work in spam ﬁltering are those at work in E-Discovery bayesian spam ﬁlters calculate the probability of a message being spam based on its contents. Unlike simple content-based ﬁlters, Bayesian spam ﬁltering learns from spam and from good mail, resulting in a very robust, adapting and efﬁcient anti-spam approach that, best of all, returns hardly any false positives.
24. 24. Bayes Rule In Spam Filtering Type 1 vs. Type 2 Error Trade Off: Type 1 = False positive (convict someone /something that is innocent) Type 2 = False Negative (Fail to convict someone /something that is Guilty) Which Would We Rather Have in This Context?
25. 25. Bayes Rule In Spam Filtering Type 1 vs. Type 2 Error Trade Off: Type 1 = False positive (convict someone /something that is innocent) Type 2 = False Negative (Fail to convict someone /something that is Guilty) Which Would We Rather Have in This Context? Allow Some Messages to Go Your Inbox
26. 26. Bayes Rule In Spam Filtering Basic Scoring Content-based spam ﬁlter looks for words and other characteristics typical of spam. Every characteristic element is assigned a score, and a spam score for the whole message is computed from the individual scores. Some scoring ﬁlters also look for characteristics of legitimate mail, lowering the complete score =
27. 27. Train the Filter = In light of what you have now identiﬁed as spam, update the scoring methods or properties that the spam ﬁlter uses Wisdom of Crowds --> Leverage large data set to see what crowd thinks is spam
28. 28. Example From a Info Tech Company http://www.bluewatermedia.com/support/spam-ﬁlter.html
29. 29. Bayes Rule In Spam Filtering http://www.bluewatermedia.com/support/spam-ﬁlter.html
30. 30. Keep Thinking About the Relationship between Spam Filters and EDiscovery / Automated Doc Review
31. 31. Daniel Martin Katz @ computational computationallegalstudies.com lexpredict.com danielmartinkatz.com illinois tech - chicago kent college of law@