Listage [p]

The occupancy at my house will soon triple, but this weekend it might double (in which case next weekend it would only double). Now I can handle the simple stuff like installing a dog door and removing poisonous shrubbery, but I'm fairly perplexed on how to go about naming the pup. There are so many approaches so I thought I'd cover a few name schemes and what would be the resultant dog name.

• Generic: Rover
  
• Self-evident: Dog
• Cute: Biscuit
• Mythological: Argos
  
• Gamer: Dogmeat
  
• Thug: Snoop
  
• Redneck: Trigger
  
• Cultural: Wilhelm
• Cultural-redneck: Aug
• MSTie: Croooooow
• Scrubs-ian: Rowdy
  
• Orwellian: Jessie
• Overloaded: Sebastian
• Nihilist:
• Delicious: Taco

We'll see how it goes. In other news The Heidi Report is gracing us all with her presence this weekend. Since there will be all kinds of people and events, I thought I'd dust off the old appearance probability algorithm. Or algorithms, we couldn't really decide on an accurate model, but such is the case when you ask the same question of a neurologist, an EE, and a software guy (walk into a bar...).

CR:
For extra credit, find out the P(W|C), since he did not supply us with the probability. You'll also need to know that P(C) = 0.5 and P(W) = 0.3.

W:
P(W|C) = 0.3
What did CS come up with? Since she is EE, she may have applied Maxwell's equations to solve this dinner conundrum. This is some ill J-Pod humor.

CR:
I got 0.42, P(C|W) * P(W) / P(C) = 0.7 * 0.3 / 0.5 = 0.42
Yeah CS, let's see the Bode plot of the W/Chris transfer function.

CS:
My attempt at pseudo-fuzzy interfacing...

Mamdani-type or sugeno-type of fuzzy logic operands shall we use? I don't think the output function is linear or constant, so that foregoes sugeno.
If prob(C) = 0.5 & prob(W) = 0.3, then P(W|C) can be calculated by fuzzy logic's probabilistic OR.

a=newfis('w_chris_cacluator');
a.input(1).name='w';
a.input(1).range=[0 10];
a.input(1).mf(1).name='no_show';
a.input(1).mf(1).type='gaussmf';
a.input(1).mf(1).params=[1.5 0];
a.input(1).mf(2).name='maybe';
a.input(1).mf(2).type='gaussmf';
a.input(1).mf(2).params=[1.5 5];
a.input(1).mf(3).name='yes';
a.input(1).mf(3).type='gaussmf';
a.input(1).mf(3).params=[1.5 10];
a.input(2).name='chris';
a.input(2).range=[0 10];
a.input(2).mf(1).name='no_show';
a.input(2).mf(1).type='trapmf';
a.input(2).mf(1).params=[-2 0 1 3];
a.input(2).mf(2).name='maybe';
a.input(2).mf(2).type='trapmf';
a.input(2).mf(2).params=[7 9 10 12];
a.output(1).name='dinner';
a.output(1).range=[0 30];
a.output(1).mf(1).name='okay'
a.output(1).mf(1).type='trimf';
a.output(1).mf(1).params=[0 5 10];
a.output(1).mf(2).name='average';
a.output(1).mf(2).type='trimf';
a.output(1).mf(2).params=[10 15 20];
a.output(1).mf(3).name='fabulous';
a.output(1).mf(3).type='trimf';
a.output(1).mf(3).params=[20 25 30];
a.rule(1).antecedent=[1 1];
a.rule(1).consequent=[1];
a.rule(1).weight=1;
a.rule(1).connection=2;
a.rule(2).antecedent=[2 0];
a.rule(2).consequent=[2];
a.rule(2).weight=1;
a.rule(2).connection=1;
a.rule(3).antecedent=[3 2];
a.rule(3).consequent=[3];
a.rule(3).weight=1;
a.rule(3).connection=2

Boil down to... fuzzy probabilities of probor(C,W) = C+W-CW.

But what.. what is C and W membership function?