Jason House wrote:
On Dec 12, 2007 3:09 PM, Álvaro Begué <[EMAIL PROTECTED]
<mailto:[EMAIL PROTECTED]>> wrote:
On Dec 12, 2007 3:05 PM, Jason House <[EMAIL PROTECTED]
<mailto:[EMAIL PROTECTED]>> wrote:
On Dec 12, 2007 2:59 PM, Rémi Coulom
<[EMAIL PROTECTED]
<mailto:[EMAIL PROTECTED]>> wrote:
> Do you mean a plot of the prediction rate with only the
> gamma of interest varying?
No the prediction rate, but the probability of the
training data. More
precisely, the logarithm of that probability.
I still don't know what you mean by this.
He probably should use the word "likelihood" instead of
"probability". http://en.wikipedia.org/wiki/Likelihood_function
Clearly I'm missing something, because I still don't understand.
Let's take a simple example of a move is on the 3rd line and has a
gamma value of 1.75. What is the equation or sequence of discrete
values that I can take the derivative of?
Doing conditional probabilities based on "move is on 3rd line" and
"move is selected" (AKA pure training data) seems to yield a fixed
value rather than something approximating a normal distribution.
Consider, in the Elo rating analogy, a player with a win and a loss to
player whose gamma is 1. There you have P(gamma)=gamma/(1+gamma)², whose
maximum is at gamma = 1. It is that probability that I am talking about.
It is the probability that is maximized by the MM algorithm.
Rémi
_______________________________________________
computer-go mailing list
computer-go@computer-go.org
http://www.computer-go.org/mailman/listinfo/computer-go/
