# Bayesian Decision Theory

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CHAPTER 3:
Bayesian Decision
Theory
Souce: Alpaypin with modifications by
Christoph F. Eick;
Remark: Belief Networks will be covered
in April. Utility theory will be covered as part of
reinforcement learning.
Probability and Inference




Result of tossing a coin is {Heads,Tails}
Random var X {1,0}
Bernoulli: P {X=1} = po
Sample: X = {xt }Nt =1
Estimation: po = # {Heads}/#{Tosses} = ∑t xt / N
Prediction of next toss:
Heads if po &gt; &frac12;, Tails otherwise
In the theory of probability and statistics, a Bernoulli trial is an experiment whose
outcome is random and can be either of two possible outcomes, &quot;success&quot; and &quot;failure&quot;.
P(X=k)=
2
Binomial Distribution
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Lecture Notes for E Alpaydın 2004 Introduction to Machine Learning &copy; The MIT Press (V1.1)
Classification
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

Credit scoring: Inputs are income and savings.
Output is low-risk vs high-risk
Input: x = [x1,x2]T ,Output: C  {0,1}
Prediction:
C  1 if P (C  1 | x 1,x 2 )  0.5
choose 
C  0 otherwise
or equivalent ly
C  1 if P (C  1 | x 1,x 2 )  P (C  0 | x 1,x 2 )
choose 
C  0 otherwise
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Lecture Notes for E Alpaydın 2004 Introduction to Machine Learning &copy; The MIT Press (V1.1)
Bayes’ Rule
prior
posterior
likelihood
P C  p x | C 
P C | x  
p x 
evidence
P C  0  P C  1  1
p x   p x | C  1P C  1  p x | C  0P C  0
p C  0 | x   P C  1 | x   1
see: http://en.wikipedia.org/wiki/Bayes'_theorem
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Bayes’ Rule: K&gt;2 Classes
p x | Ci P Ci 
P Ci | x  
p x 
p x | Ci P Ci 
 K
 p x | Ck P Ck 
k 1
K
P Ci   0 and  P Ci   1
i 1
c hoose Ci if P Ci | x   maxk P Ck | x 
Remember: The disease/symptom example
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Losses and Risks

Actions: αi
Loss of αi when the state is Ck : λik

Expected risk (Duda and Hart, 1973)

K
R i | x    ik P Ck | x 
k 1
c hoosei if R i | x   mink R k | x 
Remark:
λik is the cost of choosing i when k is correct!
If we use accuracy/error, then
λik := If i=k then 0 else 1!
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Losses and Risks: 0/1 Loss
0 if i  k
ik  
1 if i  k
K
Ri | x    ik P Ck | x 
k 1
  P Ck | x 
k i
 1  P Ci | x 
For minimum risk, choose the most probable class
Remark: This strategy is not optimal in other cases
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Losses and Risks: Reject
0 if i  k

ik   if i  K  1, 0    1
1 otherwise

Risk for
reject
K
R  K 1 | x    P Ck | x   
k 1
R i | x    P Ck | x  1  P Ci | x 
k i
choose Ci
if P Ci | x   P Ck | x  k  i and P Ci | x   1  
reject
otherwise
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Example and Homework!
C1=has cancer
C2=has not cancer
12=9
21=72
Homework:
a) Determine the optimal decision making strategy
Inputs: P(C1|x), P(C2|x)
Decision Making Strategy:…
b) Now assume we also have a reject option and the cost for making no
decision are 3:
reject,2=3
reject, 1=3
Inputs: P(C1|x), P(C2|x)
Decision Making Strategy: …
Ungraded Homework: to be discussed Feb. 6!
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Homework:
a) Determine the optimal decision making strategy
Input: P(C1|x),
R(1|x)=9xP(C2)
R(2|x)=72xP(C1)
R(reject|x)=3
Setting those equal receive:
9xP(C2)=72xP(C1) (P(C2)/P(C1))=8; additionally using P(C1)+P(C2)=1
we receive: P(C1)=1/9 and P(C2)=8/9 and the risk-minimizing decision
rule becomes: IF P(C1)&gt;1/9 THEN choose C1 ELSE choose C2
b) Now assume we also have a reject option and the cost for making no
decision are 3:
reject,2=3
reject, 1=3
Input: P(C1|x)
First we find equating R(reject|x) with R(1|x) and R(2|x):
If P(C2)≥1/3  P(C1) ≤2/3 reject should be preferred over class1 and
P(C1)≥1/24 reject should be preferred over class2. Combining this
knowledge with the previous decision rule we receive:
IF P(C1)[0,1/24] THEN choose class2
ELSE IF P(C1)[2/3,1] THEN choose class1
ELSE choose reject
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Discriminant Functions
c hooseCi if gi x   maxk gk x 
gi x , i  1,, K
 R i | x 

gi x   P C i | x 
p x | C P C 
i
i

K decision regions R1,...,RK
Ri  x | gi x   maxk gk x 
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Lecture Notes for E Alpaydın 2004 Introduction to Machine Learning &copy; The MIT Press (V1.1)