Jason J. Corso, , SUNY Buffalo Solutions by Yingbo Zhou

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Jason J. Corso, [email protected], SUNY Buffalo
Solutions by Yingbo Zhou
CSE 455/555 Spring 2013 Quiz 7 of 14
Name:
ID#:
Section:
455
or
2
555
8
10
Directions – The quiz is closed book/notes. You have 10 minutes to complete it; use this paper only.
Problem 1: Recall (2pts) (Answer in one sentence only.)
What is the maximum likelihood estimate of parameter θ given some data D?
Solution:
The maximum likelihood estimate of the parameter θ for some data D is the parameter θ̂ that maximize the likelihood.
Problem 2: Work (8 pts) (Show all derivations/work and explain.)
Suppose that we have collected some data X = {x1 , x2 , . . . , xN }. Assume that the data we collected follow the Poisson
distribution:
λx exp{−λ}
p(x; λ) =
x!
Write the likelihood function and estimate the parameter λM L using maximum likelihood parameter estimation.
Hint: log-likelihood function might be easier for derivation.
Solution:
L(λ; X) =
N
Y
p(xn ; λ) =
n=1
N
Y
λxn exp{−λ}
xn !
n=1
The log-likelihood:
l(λ; X) =
N
X
{xn log λ − λ − log xn !}
n=1
Take the partial derivative w.r.t. λ and set to zero:
∂l(λ; X)
=0
∂λ
PN
n=1 xn
−N =0
λ
PN
xn
λM L = n=1
N
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