Maarten's answer is deeper than mine. However,
if your model is at all appropriate this trick
might work.
Nick
[email protected]
Nick Cox
> I don't know about "best", particularly in general.
>
> But one way to do it is to parameterise in terms
> of e.g. a, b, 1 - (a + b). If you are lucky this
> will be enough.
>
> You could still have problems if the algorithm
> is happiest at e.g. a = 1.1, b = -0.9, c = 0.8.
Deepankar Basu
> > I am trying to do a maximum likelihood estimation where some of the
> > parameters of the model are probabilities; hence they
> should add up to
> > unity.
> >
> > What is the best way to put restrictions on the relevant
> > parameters and
> > constrain them to add up to unity?
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