[SciPy-User] fast small matrix multiplication with cython?
Charles R Harris
charlesr.harris@gmail....
Tue Dec 7 17:45:22 CST 2010
On Tue, Dec 7, 2010 at 10:39 AM, Skipper Seabold <jsseabold@gmail.com>wrote:
> On Tue, Dec 7, 2010 at 12:17 PM, Charles R Harris
> <charlesr.harris@gmail.com> wrote:
> >
> >
> > On Tue, Dec 7, 2010 at 10:05 AM, <josef.pktd@gmail.com> wrote:
>
> <snip>
>
> >> It's still a linear filter, non-linear optimization comes in because
> >> the exact loglikelihood function for ARMA is non-linear in the
> >> coefficients.
> >> (There might be a way to calculate the derivative in the same loop,
> >> but that's a different issue.)
> >>
> >
> > The unscented Kalman filter is a better way to estimate the covariance of
> a
> > non-linear process, think of it as a better integrator. If the
> propagation
> > is easy to compute, which seems to be the case here, it will probably
> save
> > you some time. You might even be able to use the basic idea and skip the
> > Kalman part altogether.
> >
> > My general aim here is to optimize the algorithm first before getting
> caught
> > up in the details of matrix multiplication in c. Premature optimization
> and
> > all that.
> >
>
> Hmm I haven't seen this mentioned much in what I've been reading or
> the documentation on existing software for ARMA processes, so I never
> thought much about it. I will have a closer look. Well, google turns
> up this thread...
>
>
I've started reading up a bit on what you are doing and the application
doesn't use extended Kalman filters, so the suggestion to use unscented
Kalman filters is irrelevant. Sorry about that ;) I'm still wading through
the various statistical notation thickets to see if there might be a better
form to use for the problem but I don't see one at the moment.
Chuck
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