On 3/17/09, Athanasios Anastasiou <athanastasiou at googlemail.com> wrote:
> Recommendation #1 was OK, but i lost you in #2 :-D
> I need to do a bit more reading about simplification rules.
Well, Maxima has two modes for generating new expressions from
old ones; one is substitution of something for a symbol (this is called
evaluation) and the other is application of formal identities
(this is called simplification). For the most part simplification is
carried out by Lisp code but there is a user-visible interface into
the simplification system via tellsimp and friends.
Take a look at the reference documentation for matchdeclare
and tellsimpafter. (It is pretty dense, but I don't know any simpler
way to explain it.)
> "Continuous distribution, continuous parameter space". It starts by the
> product of the pdf values for each X[i] (This is alright) but it ends up
> with a sum in the exponent of %e (???).
Well, it's customary to pronounce a magical incantation,
"identical independent distribution" which causes the joint
density of all the data to factor into the product of the density
for each datum (and these terms have all the same form).
So if each term is like exp(foo[i]) then the product of them is
like exp(sum(foo[i], ...)). It's often easier to work with the
logarithm of that, so you get just the sum.
Hope this helps. By the way if you would like an introductory
text, I recommend "Bayesian Data Analysis" by Gelman, Carlin,
Stern, & Rubin.
best
Robert Dodier