Large-scale Atomistic Modeling of Laser-assisted Surface Nanostructuring
MATH6630 Assignment 2 by Weiqing Fang. Multifunctional Materials | Micro-and-Nanostructuring...
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Transcript of MATH6630 Assignment 2 by Weiqing Fang. Multifunctional Materials | Micro-and-Nanostructuring...
MATH6630 Assignment 2Applicati on of EM Algorithm
In study of HIV infecti on
b y Weiqing Fang
Multifunctional Materials | Micro-and-Nanostructuring Laboratory Slide 2
Introduction
IntroductionIntroduction
Slide 3
Sexual behavior of individuals ate risk for HIV infectionSuppose 1500 gay men were surveyed and each was asked how many risky sexual encounters he had in the previous 30 days. Let ni denote the number of respondents reporting i encounters. The reporting numbers are summarized in following table.
i 0 1 2 3 4 5 6 7 8
ni 379 299 222 145 109 95 73 59 45
i 9 10 11 12 13 14 15 16
ni 30 24 12 4 2 0 1 1
IntroductionIntroduction
Slide 4
: Probability of people representing typical behavior, who respond truthfully and their numbers of risky encounters following Poission()
: Probability of people report zero risky encounters even if this is not true.
1- -: Probability of people representing high-risk behavior, who respond truthfully and their numbers of risky encounters following Poission()
E - Step
E - step
Slide 6
Likelihood function
16
0 160
( )( | n ,..., n )
!
ini
i
Li
{i 0}( ) 1 (1 )i ii e e
Where
[ ]
E – step: Compute Q(|(t))E - step
Slide 7
( )0 (t)
0
( | )( )
tZ
( )( )
( | )( )
it
i ti
et
( )( )
(1 )( | )
( )
it
i ti
ep
Probability the i risky encounter belong to zero group:
Probability the i risky encounter belong to typical group:
Probability the i risky encounter belong to high-risk group:
M - Step
M – Step: Maximize Q(|(t)) M - Step
Slide 9
• Derive the 1st order partial derivatives of Q(|(t)) respect to
• Set 1st order partial derivatives to zero and solve (t+1) equal to maximizer of
Derive 1st order partial derivativesM - Step
Slide 10
( ) 160( ) ( )
10
( | )(1 ) ( )
( ) ( )
tii
t ti i
n nQe e
( ) 16
( )0
( | )( )
( )
ti ii
ti i
nQe e
( ) 161
( )0
( | )( )
( )
ti ii
ti i
nQi e e
( ) 161
( )0
( | )(1 )( )
( )
ti ii
ti i
nQi e e
Solve (t+1) equal to maximizer of M - Step
Slide 11
( )( 1) 0 0 ( )tt n z
N
( )16( 1)
0
( )tt i i
i
n t
N
16( )
( 1) 016
( )
0
( )
( )
ti i
t i
ti i
i
in t
n t
16( )
( 1) 016
( )
0
( )
( )
ti i
t i
ti i
i
in p
n p
Update [ ]
Results obtained using RM - Step
Slide 12
R1 0.2367108 0.5982755 3.7583188 7.9695942 0.21686172 0.18435487 0.64339931 3.83912238 7.93340085 0.012713743 0.17445448 0.64914307 3.85652696 7.78296758 0.017181914 0.17214260 0.64726307 3.85377345 7.69155469 0.010503075 0.171290563 0.644548457 3.845279737 7.642039844 0.0058313116 0.170665118 0.642305767 3.836370909 7.613279770 0.0035192357 0.170094226 0.640599481 3.828644860 7.594382538 0.0023967358 0.169588178 0.639297710 3.822346057 7.580625490 0.0017811959 0.16916086 0.63828644 3.81731303 7.57001612 0.0013849910 0.168810776 0.637489459 3.813311140 7.561629160 0.00109757811 0.168528271 0.636856228 3.810128908 7.554942270 0.000875622
Estimate standard errors
Standard ErrorEstimate standard errors
Slide 14
10 0
T1
0
0, I( )
( | ) ( | )I( ) i i
ni
N
l n l n
Use the sample covariance of the first derivate, instead of calculating the second derivative.
Slide 15
Questions ?