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report1.txt
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report1.txt
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For each of the following I used a 10,000 burn period, then took 10,000 samples.
I list the results first, then specify the networks below.
# Results:
## Burglar Alarm
P(Burglary | JohnCalls=true, MaryCalls=true) = < 0.2862 , 0.7138 >
P(Alarm | JohnCalls=true, MaryCalls=true) = < 0.7586 , 0.2414 >
P(Earthquake | JohnCalls=true, MaryCalls=true) = < 0.1781 , 0.8219 >
P(Burglary | JohnCalls=true, MaryCalls=false) = < 0.004 , 0.996 >
P(Burglary | JohnCalls=true) = < 0.0178 , 0.9822 >
P(Burglary | MaryCalls=true) = < 0.0566 , 0.9434 >
## My Network
P(Cross | Diesel=true, Diesel10=true) = < 0.3417 , 0.6583 >
P(Cross | Thomas=true) = < 0.1732 , 0.8268 >
P(Diesel | Cross=true) = < 0.5777 , 0.4223 >
## Nozomu's Network
P(MapoDoufu | Burn=false) = < 0.2695 , 0.7305 >
## Steven's Network
P(AS | IA=false) = < 0.5837 , 0.4163 >
P(AS | IA=false) = < 0.2972 , 0.7028 >
## Jared's Network
P(CK | DR=false) = < 0.5106 , 0.4894 >
P(IT | CK=true) = < 0.8367 , 0.1633 >
#Networks:
## Thomas (mine)
P(Thomas=t) = 0.85
P(Percy=t) = 0.45
P(Diesel=t) = 0.3
P(Diesel10=t) = 0.1
P(BadAdvice=t | Diesel=t, Diesel10=t) = 0.99
P(BadAdvice=t | Diesel=t, Diesel10=f) = 0.89
P(BadAdvice=t | Diesel=f, Diesel10=t) = 0.92
P(BadAdvice=t | Diesel=f, Diesel10=f) = 0.2
P(Fire=t | Diesel10=t) = 0.03
P(Fire=t | Diesel10=f) = 0.001
P(ThomasRationalizes=t | BadAdvice=t) = 0.6
P(ThomasRationalizes=t | BadAdvice=f) = 0.1
P(PercyRationalizes=t | Thomas=t, BadAdvice=t) = 0.2
P(PercyRationalizes=t | Thomas=t, BadAdvice=f) = 0.01
P(PercyRationalizes=t | Thomas=f, BadAdvice=t) = 0.5
P(PercyRationalizes=t | Thomas=f, BadAdvice=f) = 0.05
P(Accident=t | ThomasRationalizes=t, PercyRationalizes=t, Diesel=t) = .87
P(Accident=t | ThomasRationalizes=t, PercyRationalizes=t, Diesel=f) = .45
P(Accident=t | ThomasRationalizes=t, PercyRationalizes=f, Diesel=t) = .5
P(Accident=t | ThomasRationalizes=t, PercyRationalizes=f, Diesel=f) = .1
P(Accident=t | ThomasRationalizes=f, PercyRationalizes=t, Diesel=t) = .32
P(Accident=t | ThomasRationalizes=f, PercyRationalizes=t, Diesel=f) = .07
P(Accident=t | ThomasRationalizes=f, PercyRationalizes=f, Diesel=t) = .2
P(Accident=t | ThomasRationalizes=f, PercyRationalizes=f, Diesel=f) = .03
P(ReallyUseful=t | Thomas=t, Percy=t, Accident=t) = 0.2
P(ReallyUseful=t | Thomas=t, Percy=t, Accident=f) = 0.9
P(ReallyUseful=t | Thomas=t, Percy=f, Accident=t) = 0.05
P(ReallyUseful=t | Thomas=t, Percy=f, Accident=f) = 0.6
P(ReallyUseful=t | Thomas=f, Percy=t, Accident=t) = 0.03
P(ReallyUseful=t | Thomas=f, Percy=t, Accident=f) = 0.55
P(ReallyUseful=t | Thomas=f, Percy=f, Accident=t) = 0.03
P(ReallyUseful=t | Thomas=f, Percy=f, Accident=f) = 0.5
P(ConfusionDelay=t | Accident=t, Fire=t) = .999
P(ConfusionDelay=t | Accident=t, Fire=f) = .69
P(ConfusionDelay=t | Accident=f, Fire=t) = .9
P(ConfusionDelay=t | Accident=f, Fire=f) = .02
P(Cross=t | ReallyUseful=t, ConfusionDelay=t) = 0.55
P(Cross=t | ReallyUseful=t, ConfusionDelay=f) = 0.01
P(Cross=t | ReallyUseful=f, ConfusionDelay=t) = 0.9
P(Cross=t | ReallyUseful=f, ConfusionDelay=f) = 0.15
## Burn (Nozomu)
P(AdobadoBurrito=true) = 0.05
P(PadKaPrao=true) = 0.01
P(MapoDoufu=true) = 0.3
P(Burn=true | AdobadoBurrito=true, PadKaPrao=true, MapoDoufu=true) = 0.9
P(Burn=true | AdobadoBurrito=true, PadKaPrao=true, MapoDoufu=false) = 0.8
P(Burn=true | AdobadoBurrito=true, PadKaPrao=false, MapoDoufu=true) = 0.7
P(Burn=true | AdobadoBurrito=true, PadKaPrao=false, MapoDoufu=false) = 0.3
P(Burn=true | AdobadoBurrito=false, PadKaPrao=true, MapoDoufu=true) = 0.55
P(Burn=true | AdobadoBurrito=false, PadKaPrao=true, MapoDoufu=false) = 0.5
P(Burn=true | AdobadoBurrito=false, PadKaPrao=false, MapoDoufu=true) = 0.2
P(Burn=true | AdobadoBurrito=false, PadKaPrao=false, MapoDoufu=false) = 0.01
## Home or School? (Steven)
SFB = Surfing Facebook,
AH = At Home,
AS = At School,
IA = I'm awake,
BC = Birds Chirping,
LO = Light Outside
P(SFB=true) = 0.5
P(AH=true | SFB=true) = 0.8
P(AH=true | SFB=false) = 0.7
P(AS=true | SFB=true) = 0.4
P(AS=true | SFB=false) = 0.6
P(IA=true | AH=true, AS=true) = 0.01
P(IA=true | AH=true, AS=false) = 0.24
P(IA=true | AH=false, AS=true) = 0.63
P(IA=true | AH=false, AS=false) = 0.95
P(BC=true) = 0.68
P(LO=true | IA=true, BC=true) = 0.96
P(LO=true | IA=false, BC=true) = 0.73
P(LO=true | IA=true, BC=false) = 0.14
P(LO=true | IA=false, BC=false) = 0.02
## Dirty Roommates (Jared)
DR = Dirty Roommates
IT = I'm Tired
SDD = someone has done dishes lately
CC = cleaning checks in the last 3 days
CK = clean kitchen
P(DR = True) = .7
P(IT = True) = .8
P(SDD = True | DR = True, IT = True) = .8
P(SDD = True | DR = True, IT = False) = .4
P(SDD = True | DR = False, IT = True) = .6
P(SDD = True | DR = False, IT = False) = .9
P(CC = True) = .09
P(CK = True | SDD = True, CC = True) = .99
P(CK = True | SDD = True, CC = False) = .7
P(CK = True | SDD = False, CC = True) = .5
P(CK = True | SDD = False, CC = False) = .05