On 01/30/2011 09:23 PM, Ted Dunning wrote:
I.e. are the samples you have from a truncated normal distribution where you don't know the truncation point exactly?
Yes - and the points are always on the left side of the curve starting at zero (So the mean is always greater than zer0)..
In the former case, I would define three parameters, mean, standard deviation and truncation point. Mean is unconstrained, standard deviation is bounded to be positive and truncation is bounded to be equal to or larger than you largest sample. Then use almost any optimization technique to minimize maximum absolute deviation of you empirical cumulative distribution versus the computed version of the truncated distribution. This should be a very well behaved optimization that doesn't need any gradient information to succeed.
Thanks - I'll have a look at some of the other optimizers and give it a whirl. I need to learn how to use the LM Optimizer for another task as well, so I thought this might be a good "Learning case". I'm starting to see a few flickers of light now, so I'll post back if I get stuck somewhere. Thanks again, - Ole --------------------------------------------------------------------- To unsubscribe, e-mail: user-unsubscr...@commons.apache.org For additional commands, e-mail: user-h...@commons.apache.org
