Hi All,
I would like to know, is there a *ballpark* figure for how many
parameters the minimisation routines can cope with?
I'm asking because I was asked if I knew.
Cheers,
Federico
--
Federico C. F. Calboli
Department of Epidemiology and Public Health
Imperial College, St. Mary's Campus
Federico Calboli f.calboli at imperial.ac.uk writes:
Hi All,
I would like to know, is there a *ballpark* figure for how many
parameters the minimisation routines can cope with?
I think I would make a distinction between theoretical
and practical limits. A lot depends on how fast
It really depends on how well-behaved your objective function is, but I've been
able to fit a few models with 10--15 parameters. But I felt like I was
stretching the limit there.
-roger
Federico Calboli wrote:
Hi All,
I would like to know, is there a *ballpark* figure for how many
Applications with lots of parameters also tend to have parameters in
a relatively small number of families, and each of these few families
could be considered to have a distribution. Splines, for example, have
lots of parameters -- sometimes more parameters than observations (as do
Seagulls have a very different perspective to ballparks
than ants. Nonetheless, there is something that can be
said.
There are several variables in addition to the number of
parameters that are important. These include:
* The complexity of the likelihood
* The number of observations in the
I regularly optimize functions of over 1000 parameters for posterior
mode computations using a variant of newton-raphson. I have some
favorable conditions: the prior is pretty good, the posterior is
smooth, and I can compute the gradient and hessian.
albyn
On Mon, Jun 19, 2006 at 06:53:00PM
