see the pdf attached for the questions.

2/27/22, 9:36 PM COMP 494 (Revision 2) Study Guide: Unit 4

https://comp.athabascau.ca/494/r2/unit04.html 1/7

CO M PU TER S C IE N CE 4 94: R ESE A RC H M ETH O DS

Stu dy G uid e

U nit 4 : M in in g, S im ula tio n , O pt im iz a tio n a n d M ode lin g

This u n it c o n ta in s t h e f o llo w in g s e ctio ns:

4 .1 P re fa ce

4 .2 L ea rn in g O utc o m es

4 .3 D ata M ining

4.4 S im ula tio n

4.5 O ptim izatio n: Nu meric al A naly sis , Nu meric al M eth od s, O pera tio ns R ese a rc h

4 .6 M od elin g: M ath em atic al M od elin g a n d B ayesia n N etw ork s

4 .7 R efe re n ce s

4 .1 P re fa ce

This u n it in tr o d u ce s f o u r u n iqu e c o m pu ta tio nal t e ch n iq ues t h at c a n b e o f u se in c e rta in t y p es o f d ata a n aly sis : d ata m in in g,

sim ula tion , op tim iz a tion , a n d B ayesia n n etw or ks .

W e a n tic ip ate t h at y o u w ill n eed a b ou t 1 1 h ou rs ( 6 60 m in ute s) t o c o m ple te t h is u n it .

4 .2 L e arn in g O bje ctiv e s

Aft e r c o m ple tin g U nit 4 , y o u s h ou ld b e a b le t o

a n aly se p atte rn s o f d ata u sin g d ata m in ing t e ch n iq ues.

d esig n s im ula tio n e x p erim en ts .

so lv e o p tim izatio n p ro b le m s.

m od el p ro b le m s in m ath em atic al a n d c a u sa l f r a m ew ork s.

4 .3 D ata M in in g

W e e stim ate t h at t h is s e g m en t o f U nit 4 s h ou ld t a k e a b ou t 2 .5 h ou rs ( 1 5 0 m in ute s) t o c o m ple te .

T hro u gh t h e u se o f a u to m ate d d ata -m in ing t e ch n iq ues, in du str ie s a n d b u sin esse s a re d is co verin g n ew t r e n ds a n d p atte rn s o f

b eh avio r a n d d ata t h at p re v io usly w en t u n notic ed . O nce t h ey h ave u n co vere d t h is v it al in te llig en ce , it c a n b e u se d f o r a v a rie ty

o f a p plic atio ns. ( C hen , C hen , & V erm a, 2 0 10 , p . 6 5)

F or e x am ple , t h e T oro n to R ap to rs b ask etb all t e a m u se s d ata -m in ing t e ch n iq ues t o p re p are t h e t e a m a g ain st o p posin g t e a m s;

th e B an k o f M on tr e a l’s b u sin ess in te llig en ce a n d k n ow le d ge d is co very p ro gra m is u se d t o g ain in sig ht in to c u sto m er b eh avio ur. 2/27/22, 9:36 PM COMP 494 (Revision 2) Study Guide: Unit 4

https://comp.athabascau.ca/494/r2/unit04.html 2/7

Rea d in g A ssig n m en t 4 .3 .R 1

This r e a d in g p ro vid es a n in tr o d u ctio n t o d ata m in ing.

Z aïa ne, O .R . ( 1 9 99). C hap te r 1 : I n tr o d u ctio n t o d ata m in ing. R etr ie ved J an uary f r o m t h e w eb sit e o f t h e U niv ers it y o f A lb erta :

h ttp :/ /w eb d ocs.c s.u alb erta .c a /~ za ia ne/co u rs e s/ cm pu t6 90 /n ote s/ C hap te r1 / ch 1.p d f

(h ttp :/ /w eb d ocs.c s.u alb erta .c a /% 7E za ia ne/co u rs e s/ cm pu t6 90 /n ote s/ C hap te r1 / ch 1.p d f)

V ie w in g A ssig n m en t 4 .3 .V 1

The v id eo c lip s l is te d b elo w p ro vid e d em on str a tio ns o f d ata m in ing.

cre a tiv co m mIT ( 2 0 10 a). I n tr o d u ctio n t o d ata m in ing ( 1 / 3 ). R etr ie ved f r o m http :/ /y o u tu .b e/E tF Q v_ B 7Y A 8

(h ttp :/ /y o u tu .b e/E tF Q v_ B 7Y A 8)

N ote : D o n ot b e c o n ce rn ed b y t h e b la ck s c re en t h at y o u w ill s e e f o r t h e f ir st s e v era l m in ute s o f t h e v id eo . I m ag es b eg in a t

ab ou t 3 :3 5.

cre a tiv eco m mIT ( 2 0 10 b). I n tr o d u ctio n t o d ata m in ing ( 2 /3 ). R etr ie ved f r o m http :/ /y o u tu .b e/U dxD jQ neh 0 k

(h ttp :/ /y o u tu .b e/U dxD jQ neh 0 k)

cre a tiv eco m mIT ( 2 0 10 c). I n tr o d u ctio n t o d ata m in ing ( 3 /3 ). R etr ie ved f r o m http :/ /y o u tu .b e/U vB U PK T6aw 8

(h ttp :/ /y o u tu .b e/U vB U PK T6aw 8)

T ab la d il lo , M . ( 2 0 09). M icro so ft d ata m in ing d em o: F ill f r o m e x am ple . R etr ie ved f r o m http :/ /y o u tu .b e/NS yF Q PA kkq c

(h ttp :/ /y o u tu .b e/NS yF Q PA kkq c)

4 .4 S im ula tio n

W e e stim ate t h at t h is s e g m en t o f U nit 4 s h ou ld t a k e a b ou t 2 h ou rs ( 1 2 0 m in ute s) t o c o m ple te .

In g en era l, s im ula tio n is u se d t o m od el a n d a n aly ze r a n dom ness in a s y ste m . S im ula tio n m od elin g a n d a n aly sis is e sp ecia lly

u se fu l

[w hen ] it is im possib le o r e x tr e m ely e x p en siv e t o o b se rv e p ro ce sse s in t h e r e a l w orld , e .g ., n ex t y ea r’s c a n ce r s ta tis tic s,

p erfo rm an ce o f t h e n ex t s p ace s h uttle , a n d t h e e ff e ct o f I n te rn et a d vertis in g o n a c o m pan y’s s a le s.

[fo r] p ro b le m s in w hic h m ath em atic al m od els c a n b e f o rm ula te d , b u t a n aly tic s o lu tio ns a re e it her im poss ib le ( e .g ., j o b

sh op s c h ed u lin g p ro b le m , h ig her o rd er d if fe re n ce e q u atio ns), o r t o o c o m plic ate d ( e .g ., c o m ple x s y ste m s l ik e t h e s to ck

m ark et, a n d l a rg e s c a le q u eu in g m od els ).

[w hen ] it is im possib le o r e x tr e m ely e x p en siv e t o v a lid ate t h e m ath em atic al m od el d esc rib in g t h e s y ste m , e .g ., d u e t o

in su ffic ie nt d ata .

A pplic atio ns o f s im ula tio n a b ou n d in t h e a re a s o f g o vern m en t, d efe n se , c o m pu te r a n d c o m mun ic atio n s y ste m s,

m an ufa ctu rin g, t r a n sp orta tio n ( e .g ., a ir t r a ffic c o n tr o l) , h ea lt h c a re , e co lo gy a n d e n vir on m en t, s o cio lo gic al a n d

beh avio ura l s tu die s, b io sc ie nce s, e p id em iolo gy, s e rv ic es ( b an k t e lle r s c h ed u lin g), e co n om ics a n d b u sin ess a n aly sis .

(M aria , 1 9 97, p . 1 1)

R ea d in g A ssig n m en t 4 .4 .R 1

The f ir st r e a d in g b elo w p ro vid es a q u ic k in tr o d u ctio n t o s im ula tio n; t h e s e co n d d is cu sse s s im ula tio n in t h e c o n te x t o f s o cia l

re se a rc h d esig n.

T he t h ir d l is te d r e a d in g p ro vid es a v ery d eta il ed in tr o d u ctio n t o s im ula tio n. W e e n co u ra g e y o u t o b ro w se it , b e s e le ctiv e a b ou t

th ose s e ctio ns y o u c h oose t o r e a d in d eta il .

G old Sim . ( 2 0 11). I n tr o d u ctio n: W hat is s im ula tio n? R etr ie ved f r o m http :/ /w ww.g o ld sim .c o m /W eb /In tr o d u ctio n/S im ula tio n/

(h ttp :/ /w ww.g o ld sim .c o m /W eb /In tr o d u ctio n/S im ula tio n/) 2/27/22, 9:36 PM COMP 494 (Revision 2) Study Guide: Unit 4

https://comp.athabascau.ca/494/r2/unit04.html 3/7

Tro ch im , W . & D avis , S . ( 1 9 96). C om pu te r s im ula tio ns f o r r e se a rc h d esig n. R etr ie ved f r o m

h ttp :/ /w ww.s o cia lr e se a rc h m eth od s.n et/ sim ul/ sim ul.h tm ( h ttp :/ /w ww.s o cia lr e se a rc h m eth od s.n et/ sim ul/ sim ul.h tm )

Arc h am , H . ( n .d .) . S yste m s s im ula tio n: T he s h orte st r o u te t o a p plic atio ns. R etr ie ved f r o m

h ttp :/ /h om e.u balt .e d u /n ts b ars h /sim ula tio n/sim .h tm ( h ttp :/ /h om e.u balt .e d u /n ts b ars h /sim ula tio n/sim .h tm )

Pra ctic e A ssig n m en t 4 .4 .P 1

In sta ll M IT R E C orp ora tio n’s T ortu ga ( h ttp :/ /co d e.g o ogle .c o m /p /to rtu gad es/ ) , r u n a n e x am ple f r o m t h e in tr o d u ctio n s e ctio n,

an d d esc rib e t h e s im ula tio n t e ch n iqu e e m plo yed in t h at d em o.

4 .5 O ptim iz a tio n : N um eric a l A naly sis , N um eric a l M eth od s, O pera tio n s R ese arc h

W e e stim ate t h at t h is s e g m en t o f U nit 4 s h ou ld t a k e a b ou t 2 .5 h ou rs ( 1 5 0 m in ute s) t o c o m ple te .

Nu meric a l a n aly sis is t h e s tu dy o f a lg o rit hm s u se d t o s o lv e f o r p ro b le m s in c o n tin uou s m ath em atic s, a s d is tin gu is hed f r o m

d is cre te m ath em atic s.

R ea d in g A ssig n m en t 4 .5 .R 1

The f ir st it em id en tif ie d b elo w is a b rie f in tr o d u ctio n t o n um eric al a n aly sis . T he s e co n d it em is a n e x ce lle n t c o u rs e p ag e o n

n um eric al m eth od s; w e e n co u ra g e y o u t o b ro w se t h ro u gh it , a n d k eep it in m in d f o r f u tu re r e fe re n ce , b u t d o b e s e le ctiv e a b ou t

th e p arts y o u c h oose t o r e a d .

Nu meric al a n aly sis . ( 2 0 14 , F eb ru ary 1 9 ). W ik ip ed ia . R etr ie ved M arc h 7 , 2 0 14 , f r o m http :/ /e n .w ik ip ed ia .o rg /w /in dex .p h p?

tit le = Nu meric al_ an aly sis &old id =59 613 14 73 ( h ttp :/ /e n .w ik ip ed ia .o rg /w /in dex .p h p?

tit le = Nu meric al_ an aly sis &old id =59 613 14 73 )

U niv ers it y o f S ou th F lo rid a. ( n .d .) . H olis tic n um eric al m eth od s: T ra n sfo rm in g n um eric al m eth od s e d u ca tio n f o r t h e S T E M

un derg ra d u ate . R etr ie ved J an uary 1 2 , 2 0 12 , f r o m http :/ /n um eric alm eth od s.e n g.u sf.e d u /

(h ttp :/ /n um eric alm eth od s.e n g.u sf.e d u /)

R ea d in g A ssig n m en t 4 .5 .R 1

O pera tio ns r e se a rc h . ( 2 0 14 , J an uary 2 4 ). W ik ip ed ia . R etr ie ved M arc h 7 , 2 0 14 , f r o m http :/ /e n .w ik ip ed ia .o rg /w /in dex .p h p?

tit le = O pera tio ns_ re se a rc h & old id =59 224 6 17 5 ( h ttp :/ /e n .w ik ip ed ia .o rg /w /in dex .p h p?

tit le = O pera tio ns_ re se a rc h & old id =59 224 6 17 5 )

N ote : P ay c lo se a tte n tio n t o t h e in tr o d u ctio n a n d t o t h e s e ctio ns t it le d “ O verv ie w” a n d “ P ro b le m s a d dre ss e d w ith

op era tio nal r e se a rc h .”

P ra ctic e A ssig n m en t 4 .5 .P 1

Vis it t h e NE OS s e rv er a t t h e w eb sit e o f t h e W isco n sin I n stit ute s f o r D isco very ( h ttp s:/ /n eo s-g u id e.o rg /C ase -S tu die s) , a n d s o lv e

a s a m ple o p tim izatio n p ro b le m .

4.6 M od elin g: M ath em atic a l M od eli n g a n d B aye sia n N etw ork s

W e e stim ate t h at t h is s e g m en t o f U nit 4 s h ou ld t a k e a b ou t 4 h ou rs ( 2 4 0 m in ute s) t o c o m ple te .

R ea d in g A ssig n m en t 4 .6 .R 1

The f ir st r e a d in g b elo w p ro vid es a b rie f o verv ie w o f m ath em atic al m od elin g; t h e t u to ria l t h at f o llo w s it p ro vid es a n e x ce lle n t

an d e x te n siv e in tr o d u ctio n t o t h e s u bje ct. 2/27/22, 9:36 PM COMP 494 (Revision 2) Study Guide: Unit 4

https://comp.athabascau.ca/494/r2/unit04.html 4/7

Math em atic al m od el. ( 2 0 14 , F eb ru ary 2 7). W ik ip ed ia . R etr ie ved M arc h 7 , 2 0 14 , f r o m http :/ /e n .w ik ip ed ia .o rg /w /in dex .p h p?

tit le = M ath em atic al_ m od el& old id =58 8574 34 0 ( h ttp :/ /e n .w ik ip ed ia .o rg /w /in dex .p h p?

tit le = M ath em atic al_ m od el& old id =58 8574 34 0 )

M cL au gh lin , M . P . ( 1 9 93-1 9 99). “ . . . t h e v ery g am e . . . ” : A t u to ria l o n m ath em atic al m od elin g. R etr ie ved J an uary 1 3 , 2 0 12 ,

fr o m http :/ /w ww.c a u sa sc ie ntia .o rg /m ath _ sta t/ T uto ria l.p d f ( h ttp :/ /w ww.c a u sa sc ie ntia .o rg /m ath _ sta t/ T uto ria l.p d f)

N ote : T h is t u to ria l r u ns f o r 5 0 p age s.

R ea d in g A ssig n m en t 4 .6 .R 2

Bayesia n n etw ork . ( 2 0 14 . F eb ru ary 2 3). W ik ip ed ia . R etr ie ved M arc h 7 , 2 0 14 , f r o m http :/ /e n .w ik ip ed ia .o rg /w /in dex .p h p?

tit le = B ayesia n_ netw ork & old id =59 673 4 8 37 ( h ttp :/ /e n .w ik ip ed ia .o rg /w /in dex .p h p?

tit le = B ayesia n_ netw ork & old id =59 673 4 8 37)

B ayesia n b elie f n etw ork s ( B BNs ) p ro vid e a w ay o f a n aly zin g d ata t h at is in co m ple te in o rd er t o d ra w c o n clu sio ns b ase d o n

p ro b ab il it ie s. T he q u ote s b elo w s u ggest t h e s tr e n gth a n d c o m ple x it y o f t h e t e ch n iq ue. D o n ot b e in tim idate d b y t h ese q u ote s.

T he a ssig ned r e a d in gs p ro vid e v ery c o m pre h en sib le in tr o d u ctio ns t o t h e t o p ic , a n d y o u w ill h ave t h e o p portu n it y t o w ork w ith a

B BN in t h e p ra ctic e a ssig nm en t f o r t h is s e ctio n.

B elie f n etw ork s ( a ls o k n ow n a s B ayesia n b elie f n etw ork s, B ayes n etw ork s, [ d ir ecte d a cy clic g ra p h s ( D AG s)] a n d c a u sa l

p ro b ab il is tic n etw ork s), p ro vid e a m eth od t o r e p re se n t r e la tio nsh ip s b etw een p ro p osit io ns o r v a ria ble s, e v en if t h e

re la tio nsh ip s in vo lv e u n ce rta in ty , u n pre d ic ta b il it y o r im pre cis io n. ( R oyla n ce , S p errin g, & B arra clo u gh , 2 0 01, p . 2 55)

F orm ally , B ayesia n n etw ork s a re d ir ecte d a cy clic g ra p h s w hose n od es r e p re se n t v a ria ble s, a n d w hose m issin g e d ges e n co d e

co n dit io nal in dep en den cie s b etw een t h e v a ria ble s. N od es [ c a n r e p re se n t a n y k in d o f v a ria ble : a m ea su re d p ara m ete r, a

l a te n t v a ria ble o r a h yp oth esis . T hey a re n ot r e str ic te d t o r e p re se n tin g r a n dom v a ria ble s]. E ffic ie nt a lg o rit hm s e x is t t h at

p erfo rm in fe re n ce a n d l e a rn in g in B ayesia n n etw ork s. B ayesia n n etw ork s t h at m od el s e q u en ce s o f v a ria ble s ( e .g . s p eech

sig nals o r p ro te in s e q u en ce s) a re c a lle d d yn am ic B ayesia n n etw ork s. G en era liz atio ns o f B ayesia n n etw ork s t h at c a n

re p re se n t a n d s o lv e d ecis io n p ro b le m s u n der u n ce rta in ty a re c a lle d in flu en ce d ia gra m s. ( B elie fN etw ork s, 2 0 09)

T he t e rm “ B ayesia n n etw ork s” w as c o in ed b y J u dea P ea rl in 1 9 8 5 t o e m ph asiz e t h re e a sp ects :

1. T he o ft e n s u bje ctiv e n atu re o f t h e in pu t in fo rm atio n.

2. T he r e lia nce o n B ayes’s c o n dit io nin g a s t h e b asis f o r u pd atin g in fo rm atio n.

3. T he d is tin ctio n b etw een c a u sa l a n d e v id en tia l m od es o f r e a so n in g, w hic h u n ders c o re s T hom as B ayes’ p osth um ou sly

p u blis hed p ap er o f 1 7 6 3.

In fo rm al v a ria nts o f s u ch n etw ork s w ere f ir st u se d b y l e g al s c h ola r J o h n H en ry W igm ore , in t h e f o rm o f W igm ore c h arts , t o

a n aly se s t r ia l e v id en ce in 1 9 13 . A noth er v a ria nt, c a lle d p ath d ia gra m s, w as d ev elo p ed b y t h e g en etic ist S ew all W rig ht a n d u se d

in s o cia l a n d b eh avio ra l s c ie nce s ( m ostly w ith l i n ea r p ara m etr ic m od els ).

B ayesia n n etw ork s a re u se d f o r m od elin g k n ow le d ge in c o m pu ta tio nal b io lo gy a n d b io in fo rm atic s ( g en e r e g u la to ry

n etw ork s, p ro te in s tr u ctu re , g en e e x p re ssio n a n aly sis ), m ed ic in e, d ocu m en t c la ssif ic atio n, in fo rm atio n r e tr ie va l, im ag e

p ro ce ssin g, d ata f u sio n, d ecis io n s u pport s y ste m s, e n gin eerin g, g am in g a n d l a w ( “ B ayesia n N etw ork s,” 2 0 14 ).

A B BN ca n b e a m od el o f a n y d ata se t s u ch a s a w ea th er d ata se t, a d is ea se a n d it s s y m pto m s d ata se t, a m ilit ary d ata se t, o r a

c rim inal in cid en t d ata se t. B ayesia n b elie f n etw ork s a re e sp ecia lly u se fu l w hen t h e in fo rm atio n a b ou t t h e p ast a n d/o r t h e

cu rre n t s it uatio n is v a g u e, in co m ple te , c o n flic tin g, a n d/o r u n ce rta in .

Curre n tly , v a rio us s o ft w are p ack ag es e n ab le a u se r t o b u il d a B ayesia n B elie f N etw ork ( B B N ) f o r m od elin g a p artic ula r

d ata se t [ e .g ., Ne tic a, J ava B ayes, a n d s o o n ]. ( R ie se n & G urs e l, 2 0 08)

B ayesia n b elie f n etw ork s a re p ow erfu l t o ols f o r m od elin g c a u se s a n d e ffe cts in a w id e v a rie ty o f d om ain s. . . . T hey a re

c o m pact n etw ork s o f p ro b ab il it ie s t h at c a p tu re t h e p ro b ab il is tic r e la tio nsh ip b etw een v a ria ble s, a s w ell a s h is to ric al

in fo rm atio n a b ou t t h eir r e la tio nsh ip s. ( X u, Z hen g & G uo, 2 0 0 7, p . 1 0 1) 2/27/22, 9:36 PM COMP 494 (Revision 2) Study Guide: Unit 4

https://comp.athabascau.ca/494/r2/unit04.html 5/7

Bayesia n b elie f n etw ork s m ak e e x p lic it t h e d ep en den cie s b etw een d if fe re n t v a ria ble s. I n g en era l t h ere m ay b e r e la tiv ely f e w

d ir ect d ep en den cie s ( m od ele d b y a rc s b etw een n od es o f t h e n etw ork ); t h is m ea n s t h at m an y o f t h e v a ria ble s a re

c o n dit io nally in dep en den t. ( W an g, 2 0 07, p . 2 9 )

R ea d in g A ssig n m en t 4 .6 .R 3

The f ir st t w o r e a d in gs b elo w p ro vid e v ery s im ple in tr o d u ctio ns t o B B N s; t h e t h ir d r e a d in g g iv es a b asic in tr o d u ctio n t o t h e

m ath em atic s o n w hic h B BNs d ep en d. T he f o u rth it em is a t u to ria l.

M arc o t, B .G . ( 2 0 05). W hat a re “ B ayesia n n etw ork m od els ”? R etr ie ved f r o m

h ttp :/ /w ww.p le x u so w ls .c o m /P D Fs/ W hat% 20 A re % 20 B ayesia n% 20 N etw ork % 20 M od els .p d f

(h ttp :/ /w ww.p le x u so w ls .c o m /P D Fs/ W hat% 20 A re % 20 B ayesia n% 20 N etw ork % 20 M od els .p d f)

F en to n , N. ( n .d . a). W hat is a B ayesia n n etw ork ? R etr ie ved f r o m

h ttp :/ /w ww.e ecs.q m ul.a c.u k/~ norm an /B BNs /W hat_ is _a_ B B N _.h tm

( h ttp :/ /w ww.e ecs.q m ul.a c.u k/% 7E norm an /B BN s/ W hat_ is _a_ B B N _.h tm )

Fen to n , N. ( n .d . b). B ayes r u le . R etr ie ved f r o m t h e w eb sit e o f t h e S ch ool o f E le ctr o n ic E ngin eerin g a n d C om pu te r S cie nce o f

Q ueen M ary U niv ers it y o f L on don : h ttp :/ /w ww.e ecs.q m ul.a c.u k/~ norm an /B BNs /B ayes_ ru le .h tm

( h ttp :/ /w ww.e ecs.q m ul.a c.u k/% 7E norm an /B BN s/ B ayes_ ru le .h tm )

Fen to n , N. ( n .d . c). B BN tu to ria l. R etr ie ved f r o m

h ttp :/ /w ww.e ecs.q m ul.a c.u k/~ norm an /B BNs /B B N _Tuto ria l_ _A bou t_ th is _se ctio n.h tm

( h ttp :/ /w ww.e ecs.q m ul.a c.u k/% 7E norm an /B BN s/ B BN _Tuto ria l_ _A bou t_ th is _se ctio n.h tm )

Pra ctic e A ssig n m en t 4 .6 .P 1

In sta ll Ne tic a ( h ttp :/ /w ww.n ors y s.c o m /d ow nlo ad .h tm l) , a s o ft w are p ro gra m t h at h elp s y o u d ev elo p a n d u se B ayesia n

netw ork s. Y ou w ill w ish t o t h e No rs y s t u to ria l ( h ttp :/ /w ww.n ors y s.c o m /tu to ria ls / n etic a/n t_ to c_ A .h tm ) o n u sin g Ne tic a.

1. R un t h e d em on str a tio n B BN netw ork t h at c o m es w ith N etic a, a n d w rit e a s h ort n ote o n t h e n etw ork a n d h ow it h as b een

u se d a s a m od el.

2. C rit ic ally a n aly ze t h e s h ortfa ll o f B BN mod els .

A ssig n m en t 1

R ev ie w A ssig nm en t 1 o n t h e c o u rs e h om e p ag e, a n d d o w hate v er y o u c a n a t t h is p oin t.

4 .7 R efe re n ce s

Arc h am , H . ( n .d .) . S yste m s s im ula tio n: T he s h orte st r o u te t o a p plic atio ns. R etr ie ved f r o m

h ttp :/ /h om e.u balt .e d u /n ts b ars h /sim ula tio n/sim .h tm ( h ttp :/ /h om e.u balt .e d u /n ts b ars h /sim ula tio n/sim .h tm )

Bayesia n n etw ork . ( 2 0 14 . F eb ru ary 2 3). W ik ip ed ia . R etr ie ved M arc h 7 , 2 0 14 , f r o m http :/ /e n .w ik ip ed ia .o rg /w /in dex .p h p?

tit le = B ayesia n_ netw ork & old id =59 673 4 8 37 ( h ttp :/ /e n .w ik ip ed ia .o rg /w /in dex .p h p?

tit le = B ayesia n_ netw ork & old id =59 673 4 8 37)

B elie fNe tw ork s. ( 2 0 09). A bou t B elie fNe tw ork s.

C hen , P ., C hen , I ., & V erm a, R . ( 2 0 10 , I m pro vin g a n u n derg ra d u ate d ata m in ing c o u rs e w ith r e a l- w orld p ro je cts . J ou rn al of

C ir cu it s , S yste m s, a n d C om pute rs, 2 5 (4 ), 6 2– 67. R etr ie ved f r o m http :/ /d l.a cm .o rg /ft _ gate w ay.c fm ?id =17 3 4 8 10 & ty p e= pd f

(h ttp :/ /d l.a cm .o rg /ft _ gate w ay.c fm ?id =17 3 4 8 10 & ty p e= pd f)

A ls o a va il ab le t h ro u gh t h e A U L ib ra ry S erv ic es j o u rn al d ata b ase s. ( h ttp :/ /lib ra ry .a th ab asc a u .c a /e jo u rn al.p h p) 2/27/22, 9:36 PM COMP 494 (Revision 2) Study Guide: Unit 4

https://comp.athabascau.ca/494/r2/unit04.html 6/7