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.
Nick
[email protected]
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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