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

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