a bayesian dive

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A Bayesian Dive Somik Raha, Vedika Research

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These are the slides from a talk given to Vaidya Fellows and others at the Institute of Ayurveda and Integrative Medicine (IAIM). Simple applications of Bayes' Rule show how inference can be done with clarity.

TRANSCRIPT

Page 1: A Bayesian Dive

A Bayesian DiveSomik Raha, Vedika Research

Page 2: A Bayesian Dive

Question

If someone is a haemophiliac, what is your probability that this person is a male?

If someone is a male, what is your probability that this person is a haemophiliac?

Page 3: A Bayesian Dive

Question

If someone is a haemophiliac, what is your probability that this person is a male?

If someone is a male, what is your probability that this person is a haemophiliac?

Haemophilia A (clotting factor VIII deficiency) is the most common form of the disorder, present in about 1 in 5,000–10,000 male births.

Page 4: A Bayesian Dive

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>0%#but#<=20%#

>20%#but#<=40%#

>40%#but#<=60%#

>60%#but#<=80%#

>80%#but#<100%#

100%#

Male%given%Haemophilia%

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>0%#but#<=20%#

>20%#but#<=40%#

>40%#but#<=60%#

>60%#but#<=80%#

>80%#but#<100%#

100%#

Haemophilia*given*Male*

Male given Hemophiliac (n=9) Haemophiliac given Male (n=9)

44%44%

12%

88%

12%

Page 5: A Bayesian Dive

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Male%given%Haemophilia%

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>20%#but#<=40%#

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>60%#but#<=80%#

>80%#but#<100%#

100%#

Haemophilia*given*Male*

Male given Hemophiliac (n=9) Haemophiliac given Male (n=9)

44%44%

12%

88%

12%

Page 6: A Bayesian Dive

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>20%#but#<=40%#

>40%#but#<=60%#

>60%#but#<=80%#

>80%#but#<100%#

100%#

Male%given%Haemophilia%

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0%#

>0%#but#<=20%#

>20%#but#<=40%#

>40%#but#<=60%#

>60%#but#<=80%#

>80%#but#<100%#

100%#

Haemophilia*given*Male*

Male given Hemophiliac (n=9) Haemophiliac given Male (n=9)

44%44%

12%

88%

12%

Willing to be shot if you are wrong!

Page 7: A Bayesian Dive

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>20%#but#<=40%#

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>60%#but#<=80%#

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100%#

Male%given%Haemophilia%

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>20%#but#<=40%#

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>60%#but#<=80%#

>80%#but#<100%#

100%#

Haemophilia*given*Male*

Male given Hemophiliac (n=9) Haemophiliac given Male (n=9)

44%44%

12%

88%

12%

Willing to be shot if you are wrong! And, you are wrong!

Page 8: A Bayesian Dive

Placing a 100% probability on anything implies you are willing to be shot if you are wrong.

Page 9: A Bayesian Dive

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>20%#but#<=40%#

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>60%#but#<=80%#

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Male%given%Haemophilia%

Male given Hemophiliac (n=9) Haemophiliac given Male (n=9)44%44%

12%

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Haemophilia*given*Male*

88%

12%

What if you thought that P(Haemophiliac|Male) = P(Male|Haemophiliac)?

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Male%given%Haemophilia%

44%44%

12%

instead of

Page 10: A Bayesian Dive

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>60%#but#<=80%#

>80%#but#<100%#

100%#

Male%given%Haemophilia%

Male given Hemophiliac (n=9) Haemophiliac given Male (n=9)44%44%

12%

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>20%#but#<=40%#

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>60%#but#<=80%#

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Haemophilia*given*Male*

88%

12%

What if you thought that P(Haemophiliac|Male) = P(Male|Haemophiliac)?

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Male%given%Haemophilia%

44%44%

12%

instead of

Associative Logic Error

Page 11: A Bayesian Dive

Examples of Associative Logic Error

Naseeruddin Shah in Court Scene of “Khuda Key Liye”

Deen mey daari hai, daari mey deen nahiThe faithful have beards, but the beard does not have any faith

Page 12: A Bayesian Dive

Examples of Associative Logic Error

What is the essence of Jainism?

Cultural Jains: Non-violence and vegetarianism

Page 13: A Bayesian Dive

Examples of Associative Logic Error

Mahavira

What is the essence of Jainism?

Cultural Jains: Non-violence and vegetarianism

Page 14: A Bayesian Dive

Examples of Associative Logic Error

Mahavira

What is the essence of Jainism?

Cultural Jains: Non-violence and vegetarianism

Essence: Aliveness of the Universe

Page 15: A Bayesian Dive

Examples of Associative Logic Error

Mahavira

What is the essence of Jainism?

Cultural Jains: Non-violence and vegetarianism

Essence: Aliveness of the Universe

You cannot die vs

Suicide

Page 16: A Bayesian Dive

Question

If someone has lung cancer, what is your probability that this person was a smoker?

If someone is a smoker, what is your probability that this person will get lung cancer?

Page 17: A Bayesian Dive

Smoker given Lung Cancer (n=9) Lung Cancer given Smoker (n=9)

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100%#

Smoker,(given(lung(cancer(

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Lung%cancer,%given%smoker%

What do you notice?

33%22%

44%

33%22%

33%

Page 18: A Bayesian Dive

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Male%given%Haemophilia%

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100%#

Haemophilia*given*Male*

Male given Hemophiliac (n=9) Haemophiliac given Male (n=9)

44%44%

12%

88%

12%

Smoker given Lung Cancer (n=9) Lung Cancer given Smoker (n=9)

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>60%#but#<=80%#

>80%#but#<100%#

100%#

Smoker,(given(lung(cancer(

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>60%#but#<=80%#

>80%#but#<100%#

100%#

Lung%cancer,%given%smoker%

What do you notice?

33%22%

44%

33%22%

33%

Page 19: A Bayesian Dive

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>60%#but#<=80%#

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Male%given%Haemophilia%

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Haemophilia*given*Male*

Male given Hemophiliac (n=9) Haemophiliac given Male (n=9)

44%44%

12%

88%

12%

1 in 5000 males is haemophiliac

95% of all hemophilia cases are male

Condition Gender Joint

0.01% * 95% = 0.01% * 5% =

99.99% * 50% = 99.99% * 50% =

The Math of Probability

LikelihoodPrior

Page 20: A Bayesian Dive

1 in 5000 males is haemophiliac

95% of all hemophilia cases are male

Joint

0.01% * 95% = 0.01% * 5% =

99.99% * 50% = 99.99% * 50% =

The Math of Probability

0.0095%0.0005%

49.995%49.995%

Condition Gender

LikelihoodPrior

Page 21: A Bayesian Dive

Joint

0.01% * 95% = 0.01% * 5% =

The Math of Probability

0.0095%0.0005%

?

???

Condition Gender

99.99% * 50% = 99.99% * 50% =

49.995%49.995%

LikelihoodPrior

PosteriorPre-Posterior

Page 22: A Bayesian Dive

Joint

0.01% * 95% = 0.01% * 5% =

The Math of Probability

0.0095%0.0005%

Condition Gender

99.99% * 50% = 99.99% * 50% =

49.995%49.995%

LikelihoodPrior

PosteriorPre-Posterior

0.0095%

49.995%

??

Page 23: A Bayesian Dive

Joint

0.01% * 95% = 0.01% * 5% =

The Math of Probability

0.0095%0.0005%

Condition Gender

99.99% * 50% = 99.99% * 50% =

49.995%49.995%

LikelihoodPrior

PosteriorPre-Posterior

0.0095%

49.995%

49.995%

0.0005%

Page 24: A Bayesian Dive

Joint

0.01% * 95% = 0.01% * 5% =

The Math of Probability

0.0095%0.0005%

Condition Gender

99.99% * 50% = 99.99% * 50% =

49.995%49.995%

LikelihoodPrior

PosteriorPre-Posterior

0.0095%

49.995%

49.995%

0.0005%50%

50% = 50% * ?= 50% * ?

= 50% * ?= 50% * ?

Page 25: A Bayesian Dive

Joint

0.01% * 95% = 0.01% * 5% =

The Math of Probability

0.0095%0.0005%

Condition Gender

99.99% * 50% = 99.99% * 50% =

49.995%49.995%

LikelihoodPrior

PosteriorPre-Posterior

0.0095%

49.995%

49.995%

0.0005%50%

50%99.998%

0.019%

0.001%

99.999%

Page 26: A Bayesian Dive

Joint

The Math of Probability

0.0095%0.0005%

Condition Gender

49.995%49.995%

LikelihoodPrior

PosteriorPre-Posterior

0.0095%

49.995%

49.995%

0.0005%50%

50%99.998%

0.019%

0.001%

99.999%

Page 27: A Bayesian Dive

The Math of Probability

PosteriorPre-Posterior

0.0095%

49.995%

49.995%

0.0005%50%

50%99.998%

0.019%

0.001%

99.999%

In this case, your intuition matched the math!

Page 28: A Bayesian Dive

The Math of Probability

PosteriorPre-Posterior

0.0095%

49.995%

49.995%

0.0005%50%

50%99.998%

0.019%

0.001%

99.999%

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Male%given%Haemophilia%

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Haemophilia*given*Male*

Male given Hemophiliac (n=9) Haemophiliac given Male (n=9)

44%44%

12%

88%

12%

In this case, your intuition matched the math!

Page 29: A Bayesian Dive

The Math of Probability

PosteriorPre-Posterior

0.0095%

49.995%

49.995%

0.0005%50%

50%99.998%

0.019%

0.001%

99.999%

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Male%given%Haemophilia%

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Haemophilia*given*Male*

Male given Hemophiliac (n=9) Haemophiliac given Male (n=9)

44%44%

12%

88%

12%

In this case, your intuition matched the math!

Page 30: A Bayesian Dive

Smoker given Lung Cancer (n=9)

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Smoker,(given(lung(cancer(

Now let’s work this example Instructions:1. Fill in the prior and likelihood 2. Calculate joint probability 3. Flipped tree 4. Place joints correctly 5. Calculate pre-posterior

probability (add up joints) 6. Calculate posterior probability

(divide joint by pre-posterior) 7. Report probability of lung

cancer given smoker

Put the probability you thought of over here

Page 31: A Bayesian Dive

Smoker given Lung Cancer (n=9)

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Smoker,(given(lung(cancer(

Now let’s work this example

CDC:  19.3%  of  all  Americans  are  smokers  (2010)  Na<onal  Cancer  Ins<tute:  226,000  Americans  in  2012  will  be  diagnosed  with  lung  cancer  US  Census  Bureau:  313  million  people  in  the  US  as  of  Apr  21,2012  %  with  lung  cancer:  0.07%  Lung  Cancer  Prognosis:  8.9%  of  around  25,000  lung  cancer  pa<ents  were  never  smokers;  therefore  91.1%  of  lung  cancer  pa<ents  were  smokers  

Lung Cancer given Smoker (n=9)

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Lung%cancer,%given%smoker%

33%22%

33%

Page 32: A Bayesian Dive

Bayesian mathematics is how our brain is actually wired.

It is the math of common sense.

So what’s the big deal about all this?

Core of machine learning

Spam filters

Page 33: A Bayesian Dive

Alison Gopnik, TED Talk, “What Do Babies Think?”

Turns out this is how we normally learn

Page 34: A Bayesian Dive

Alison Gopnik, TED Talk, “What Do Babies Think?”

Turns out this is how we normally learn

Page 35: A Bayesian Dive

Reflections?

Suggested further reading: “The Theory That Would Not Die”

Page 36: A Bayesian Dive

Ayurvedic probabilistic fun

Arthritis Diagnostic EngineOsteo

Rheumatoid Gout

From Vedika’s Research Labs

Page 37: A Bayesian Dive

Swelling( Symptoms(

Pain(Arthri4s(

Diges4ve(Problems(

Star4ng(Loca4on(

Effect(of(Oiling(

Tongue(Coa4ng(

Relevance Diagrams (this is an exact computational tree)

Swelling Symptoms Pain Digestive ProblemsTight, Inflamed

Inflammation & Redness

General Swelling

Cracking of joints

Loss of appetite

Skin issues

Fixed

Sharp Shooting

Present

Absent

Tongue Coating

No Tongue Coating

Page 38: A Bayesian Dive

Trace one pathway of logic

Osteo Arthritis

Rheumatoic Arth

Gout

Cracking of joints

Fixed Pain

Digestive Problems Present

pOsteo

p1p2

p3Joint

= pOsteo * p1 * p2 * p3 = pJoint

Cracking of joints

Fixed Pain

Digestive Problems Present

p1p2

p3= pJointOsteo Arthritis

Rheumatoic Arth

Gout

Flip it! pOsteo*

pOsteo* = pJointp1 * p2 * p3

Page 39: A Bayesian Dive

Let’s try it

Need a volunteer Vaidya

Rest of you please follow along and answer the questions as well

Page 40: A Bayesian Dive

Getting into continuous land

PDF and CDF

No new idea, really!

Page 41: A Bayesian Dive

The thing about binomialsToss n independent coins

p : probability of 1 heads

p(k,n) : probability of getting k heads in n tosses

p(k,n) = C(n,k) * p^k * (1-p)^(n-k)

Page 42: A Bayesian Dive

Appendix

Page 43: A Bayesian Dive

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Haemophilia*given*Male*

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Male%given%Haemophilia%

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Haemophilia*given*Male*

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Male%given%Haemophilia%

Historical (n=30) Vaidya Scientists (n = 9)

Male given Hemophilia Haemophilia given Male

Page 44: A Bayesian Dive

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Haemophilia*given*Male*

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Male%given%Haemophilia%

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Haemophilia*given*Male*

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Male%given%Haemophilia%

Historical (n=30) Vaidya Scientists (n = 9)63% 100%

Male given Hemophilia Haemophilia given Male

Page 45: A Bayesian Dive

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Haemophilia*given*Male*

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0%#

>0%#but#<=20%#

>20%#but#<=40%#

>40%#but#<=60%#

>60%#but#<=80%#

>80%#but#<100%#

100%#

Male%given%Haemophilia%

0%# 10%# 20%# 30%# 40%# 50%# 60%# 70%# 80%# 90%# 100%#

0%#

>0%#but#<=20%#

>20%#but#<=40%#

>40%#but#<=60%#

>60%#but#<=80%#

>80%#but#<100%#

100%#

Haemophilia*given*Male*

0%# 10%# 20%# 30%# 40%# 50%# 60%# 70%# 80%# 90%# 100%#

0%#

>0%#but#<=20%#

>20%#but#<=40%#

>40%#but#<=60%#

>60%#but#<=80%#

>80%#but#<100%#

100%#

Male%given%Haemophilia%

Historical (n=30) Vaidya Scientists (n = 9)63% 100%

66% 89%

Male given Hemophilia Haemophilia given Male

Page 46: A Bayesian Dive

0%# 10%# 20%# 30%# 40%# 50%# 60%# 70%# 80%# 90%# 100%#

0%#

>0%#but#<=20%#

>20%#but#<=40%#

>40%#but#<=60%#

>60%#but#<=80%#

>80%#but#<100%#

100%#

Haemophilia*given*Male*

0%# 10%# 20%# 30%# 40%# 50%# 60%# 70%# 80%# 90%# 100%#

0%#

>0%#but#<=20%#

>20%#but#<=40%#

>40%#but#<=60%#

>60%#but#<=80%#

>80%#but#<100%#

100%#

Male%given%Haemophilia%

0%# 10%# 20%# 30%# 40%# 50%# 60%# 70%# 80%# 90%# 100%#

0%#

>0%#but#<=20%#

>20%#but#<=40%#

>40%#but#<=60%#

>60%#but#<=80%#

>80%#but#<100%#

100%#

Haemophilia*given*Male*

0%# 10%# 20%# 30%# 40%# 50%# 60%# 70%# 80%# 90%# 100%#

0%#

>0%#but#<=20%#

>20%#but#<=40%#

>40%#but#<=60%#

>60%#but#<=80%#

>80%#but#<100%#

100%#

Male%given%Haemophilia%

Historical (n=30) Vaidya Scientists (n = 9)63% 100%

66% 89%

Male given Hemophilia Haemophilia given Male