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Bayesian Statistics
the theory that would not die
how Bayes' rule cracked the enigma code,hunted down Russian submarines, andemerged triumphant from two centuries ofcontroversy
McGrayne, S. B., Yale University Press, 2011
You are sitting in front of a doctor and she says …
4 million – HIV- 1,400 – HIV+
Test has a 1% error rate
If don’t have HIV then 1% of time it says you have it
If you do have HIV then 1% of time it says you don’t have it
You have been told that you have a positive test (and you don’t use intravenousdrugs recreationally or partake of risky sexual practices)
What is the probability that you actually have an HIV infection?
4 million – HIV- 1,400 – HIV+
3,960,000- 40,000+ 1,386+14-
3,960,000- 40,000+ 1,386+14-
P(HIV+|Test+) = 1,386/ (40,000 + 1,386)
= 3.35%P(HIV+|Test-) = 14/ (3,960,000 + 14)
= 3.5x10-4%
P(HIV+) = 1,400 / (1,400 + 4,000,000) = 0.035%
Before the test
P(Test+|HIV+)
P(HIV+|Test+)
P(HIV+) – Hypothesis (hidden) = 0.03%
P(Data) - data (observed)
what we wantbut is hard toget to
99%
P(Data|Hyp)
P(Hyp|Data)
P(Hyp) – Hypothesis (hidden)
P(Data) - data (observed)
what we wantbut is hard toget to
easy to reason about
What is Bayes’ rule
P(Data|Hyp) P(Hyp) P(Hyp|Data) =
AnswerNormalization
PriorModel
∑ P(Data|H’) P(H’)
P(Data|Hyp) P(Hyp) P(Hyp|Data) =
∑ P(Data|H’) P(H’)
P(Test+|HIV+) P(HIV+)
P(HIV+|Test+) = P(Test+|HIV+) P(HIV+)+P(Test+|HIV-) P(HIV-)
99% x1,400/(1,400 + 4,000,000) P(HIV+|Test+) =
99% x1,400/(1,400 + 4,000,000)+ 1% x4,000,000/(1,400 + 4,000,000)
= 99% x1,400
99% x1,400+ 1% x4,000,000
1,386
1,386+ 40,000=
= 3.3%
P(Data|Hyp)
DataHyp Test- Test+HIV- 99% 1%HIV+ 1% 99%
P(Hyp)
HIV+ 0.035%HIV- 99.965%
P(Data|Hyp) P(Hyp) P(Hyp|Data) =
∑ P(Data|H’) P(H’)
P(Test+|HIV+) P(HIV+)
P(HIV+|Test+) = P(Test+|HIV+) P(HIV+)+P(Test+|HIV-) P(HIV-)
99% x 0.035% P(HIV+|Test+) =
99% x 0.035%+ 1% x 99.965%
= 0.0346%
0.0346% + 0.99965%
0.0346%
1.034%=
= 3.35%
Spreadsheet
P(Data|Hyp) P(Hyp) P(Hyp|Data) =
∑ P(Data|H’) P(H’)
P(Data|Hyp) P(Hyp) P(Hyp|Data) =
P(Data)
P(Data)=∑ P(Data|H’) P(H’)
P(Hyp|Data)P(Data)=P(Data|Hyp) P(Hyp)
P(Data|Hyp)
DataHyp A CA 99% 1%C 1% 99%
P(Hyp)
A 99.9%C 0.1%
Reference A
C Read
Reference A
C
A 99.9% C 0.1%
A -> A 98.9% A->C 0.999% C -> C 0.099%10-3%
A->C 0.999% C -> C 0.099%
Read
C
A->C 91% C -> C 9%
A->C -> A C->C->AA->C->C 0.91% C->C->C 8.9%
C->C->C 8.9%
A->C->C 9.25% C->C->C 90.75%
A->C->C 0.91%
P(Data|Hyp) P(Hyp)=
P(Hyp) P(D1|Hyp) P(D2|Hyp)…P(Dn|Hyp)
Spreadsheet
P(Data|Hyp)
DataHyp A CAA 99% 1%AC 50% 50%CC 1% 99%
P(Hyp)
AA 99.9%AC 0.075%CC 0.025%
Spreadsheet
Bayesian Statistics
• Simple mathematical basis• Long period before it was used widely
conceptual problems
computationally difficult (Hyp can get very large)
• Technique useful for many otherwise intractable problems