Sturla Molden molden.no> writes:
> > Yes, this is what I am computing. I am computing the pdf of a very high-
> > dimensional multivariate normal. Is there a specialized method to compute
> > this?
>
> If you use cho_solve and cho_factor from scipy.linalg, you can proceed
> like this:
>
> c
Thu, 09 Sep 2010 18:18:29 +0200, Sturla Molden wrote:
[clip]
> I hope the SciPy dev team can be persuaded to include a wrapper for
> DTRTRS in the future. It is after all extremely useful for Mahalanobis
> distances, and thus for any use of linear models in statistics.
I don't see reasons why not
> Yes, this is what I am computing. I am computing the pdf of a very high-
> dimensional multivariate normal. Is there a specialized method to compute
> this?
If you use cho_solve and cho_factor from scipy.linalg, you can proceed
like this:
cx = X - m
sqmahal = (cx*cho_solve(cho_factor(S),
Wow, this is great! Thanks for all the great questions.
Sebastian Walter gmail.com> writes:
> is it really the covariance matrix you want to invert? Or do you want
> to compute something like
> x^T C^{-1} x,
> where x is an array of size N and C an array of size (N,N)?
Yes, this is what I am c
is it really the covariance matrix you want to invert? Or do you want
to compute something like
x^T C^{-1} x,
where x is an array of size N and C an array of size (N,N)?
It would also be interesting to know how the covariance matrix gets computed
and what its condition number is, at least approxim
On Tue, Aug 31, 2010 at 4:52 PM, Dan Elliott wrote:
> David Warde-Farley cs.toronto.edu> writes:
> > On 2010-08-30, at 10:36 PM, Charles R Harris wrote:
> > I think he means that if he needs both the determinant and to solve the
> > system, it might be more efficient to do
> > the SVD, obtain the
David Warde-Farley cs.toronto.edu> writes:
> On 2010-08-30, at 10:36 PM, Charles R Harris wrote:
> I think he means that if he needs both the determinant and to solve the
> system, it might be more efficient to do
> the SVD, obtain the determinant from the diagonal values, and obtain the
> solutio
On 2010-08-30, at 10:19 PM, Dan Elliott wrote:
> You don't think this will choke on a large (e.g. 10K x 10K) covariance
> matrix?
That depends. Is it very close to being rank deficient?That would be my main
concern. NumPy/LAPACK will have no trouble Cholesky-decomposing a matrix this
big, pr
Hi Melissa,
On 30 August 2010 17:42, Melissa Mendonça wrote:
> I've been lurking for a while here but never really introduced myself.
> I'm a mathematician in Brazil working with optimization and numerical
> analysis and I'm looking into scipy/numpy basically because I want to
> ditch matlab.
W
On 2010-08-30, at 10:36 PM, Charles R Harris wrote:
> I don't see what the connection with the determinant is. The log determinant
> will be calculated using the ordinary LU decomposition as that works for more
> general matrices.
I think he means that if he needs both the determinant and to so
On Mon, Aug 30, 2010 at 8:19 PM, Dan Elliott wrote:
> Thanks for the reply.
>
> David Warde-Farley cs.toronto.edu> writes:
> > On 2010-08-30, at 11:28 AM, Daniel Elliott wrote:
> > > Large matrices (e.g. 10K x 10K)
> >
> > > Is there a function for performing the inverse or even the pdf of a
> >
On 08/31/2010 11:19 AM, Dan Elliott wrote:
> Thanks for the reply.
>
> David Warde-Farley cs.toronto.edu> writes:
>> On 2010-08-30, at 11:28 AM, Daniel Elliott wrote:
>>> Large matrices (e.g. 10K x 10K)
>>
>>> Is there a function for performing the inverse or even the pdf of a
>>> multinomial nor
Thanks for the reply.
David Warde-Farley cs.toronto.edu> writes:
> On 2010-08-30, at 11:28 AM, Daniel Elliott wrote:
> > Large matrices (e.g. 10K x 10K)
>
> > Is there a function for performing the inverse or even the pdf of a
> > multinomial normal in these situations as well?
>
> There's a fu
On Mon, Aug 30, 2010 at 3:42 PM, Melissa Mendonça wrote:
> Hi,
>
> I've been lurking for a while here but never really introduced myself.
> I'm a mathematician in Brazil working with optimization and numerical
> analysis and I'm looking into scipy/numpy basically because I want to
> ditch matlab.
On 2010-08-30, at 5:42 PM, Melissa Mendonça wrote:
> I'm just curious as to why you say "scipy.linalg.solve(), NOT
> numpy.linalg.solve()". Can you explain the reason for this?
Oh, the performance will be similar, provided you've linked against a good
BLAS.
It's just that the NumPy version do
Hi,
I've been lurking for a while here but never really introduced myself.
I'm a mathematician in Brazil working with optimization and numerical
analysis and I'm looking into scipy/numpy basically because I want to
ditch matlab.
I'm just curious as to why you say "scipy.linalg.solve(), NOT
numpy.
On 2010-08-30, at 11:28 AM, Daniel Elliott wrote:
> Hello,
>
> I am new to Python (coming from R and Matlab/Octave). I was preparing
> to write my usual compute pdf of a really high dimensional (e.g. 1
> dimensions) Gaussian code in Python but I noticed that numpy had a
> function for compu
Hello,
I am new to Python (coming from R and Matlab/Octave). I was preparing
to write my usual compute pdf of a really high dimensional (e.g. 1
dimensions) Gaussian code in Python but I noticed that numpy had a
function for computing the log determinant in these situations.
Is there a functi
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