[SciPy-dev] MCMC, Kalman Filtering, AI for SciPy?
charles.harris at sdl.usu.edu
Mon Sep 27 17:39:53 CDT 2004
Travis Oliphant wrote:
> Charles Harris wrote:
>> eric jones wrote:
>>> Where should these live?
>>> monte carlo and markov chain might fit in scipy.stats?
>> How about in monte_carlo or some such? I think there is too much stuff
>> put in odd places. Why is zeros in optimize? Makes no sense, but there
>> it is.
> The reason it is in optimize, is because the fsolve command for
> many-variable functions is a wrapper of something in minpack which
> formed the foundation for optimize.
> As you know there is a connection between optimization and finding
> zeros of functions. Later, one-variable root-finders were added to
> be near fsolve.
Yep, I know how and why it happened. I still think this would not be the
first place a naive user would look for zero finders,
but I can live with it. MCMC could also go in optimize, as it is often
used to make estimates of most probable solutions, likewise
for genetic algorithms. Some of these sorts of things have multiple
uses. I am argueing that using more top level directories may be
a simpler way of organizing things than trying to fit everything into a
few broad categories.
> We are not against reorganizations, but odd to you is not necessarily
> odd to someone else, and vice versa. So, let's just figure out were
> monte_carlo should go. I think it would go well under stats, or else
> a new AI subpackage.
> I agree that stats could use reorganization. Many routines were
> lifted from an old pstats.py file. While it has been significantly
> cleaned up, there are still problems.
> I hear various complaints occasionally about slowness in some of the
> distributions in stats. In order to improve things, these need to be
> better described. Their shouldn't be a lot of slow-down in most of
> the routines (aside from domain validity slowness). Some
> distributions don't have exactly defined cdf's or ppf's and these must
> be computed by SciPy using integration and zero-finding routines.
> This will be very slow.
> There are also several statistical-related routines available in
> special in all of their raw and speedy glory.
> -Travis O.
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