程序代写案例-P 1
1.
For a event X, the optimal classifier is

The expected loss:
oss (− |A)P (A) P (1|B)P (B) P (1|C)P (C) 0.19 l = P 1 + + =

2.
To compute decision boundary:
Let (1|x) P (− |x) P > 1 ⇒
Get .4 e .6 e0 * 1√2π
− 2
x2 > 0 * 1√2π
− 2
(x−3)2

n(0.4) x /2 ln(0.6) (x ) /2l − 2 > − − 3 2 ⇒
1.36484496x <

Therefore the optimal bayes classifier is


To compute Bayes risk,
isk in(0.4 (x|1), 0.6 (x| )) dxr = ∫
+∞
−∞
m * P * P − 1
risk 0.6 (x| ) dx .4 (x|1) dx ⇒ = ∫
1.3648
−∞
* P − 1 + ∫
+∞
1.3648
0 * P
risk 0.0651⇒ =

3.
Let (X |1) N (0, ), P (X | ) N (3, ), P (X) 0.4 P (X |1) .6 P (X | )P ~ 1 − 1 ~ 1 = * + 0 * − 1
P (1|X) P (X |1)P (1) / P (X)⇒ =
The expected loss is,
oss (1|x) (1 P (1|x)) P (x) dx 0.0961l = ∫
+∞
−∞
2 * P * − * =
Empirical loss is 0

4. The analysis is flawed because many assumptions are not sound. Need to elaborate on
your analysis of the paper’s argument.

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