[AstroPy] Maximum likelihood fit to unbinned data
Wed Dec 26 17:15:32 CST 2012
thanks for the references. I managed to solve the issue using the reference
I was trying to fit a density profile to a distribution of star. The usual
way of doing it is to build a histogram of dN/dR (number of points/radial
bins). But that introduces biases due to the binning choice.
A way to avoid binning is to use a likelihood approach. So I followed this
If anyone is interested in the code I wrote for that please ask!
Cheers and thanks again.
-- Eduardo Balbinot
On Sat, Dec 22, 2012 at 9:14 PM, Paul Barrett <email@example.com> wrote:
> What type of data are you trying to fit; time series, spectral or image?
> You might want to take a look at what the gamma-ray community has
> done, since almost all of their analysis involves photon lists. You
> could also try searching the astrostatistics website.
> Personally, I would use a Bayesian approach. I have successfully used
> the Nested Sampling approach to fit FUSE and Chandra spectra. This
> algorithm succeeded where the traditional Levenberg-Marquardt
> algorithm did not. The algorithm is described in "Data Analysis: A
> Bayesian Approach" by D.S. Sivia. One of the best introductions to
> the subject in my opinion.
> -- Paul
> On Sat, Dec 22, 2012 at 1:22 PM, Eduardo Balbinot
> <firstname.lastname@example.org> wrote:
> > A bit off-topic, but....
> > Anyone could point to a good guide to fitting unbinned data using a
> > likelihood estimator?
> > It would be a nice idea to have it on astropy with most used density
> > profiles (Hubble, Plummer, King, etc...). If I manage to do it we could
> > discuss including it on astropy.
> > Thanks!
> > -- Eduardo Balbinot
> > _______________________________________________
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> > AstroPy@scipy.org
> > http://mail.scipy.org/mailman/listinfo/astropy
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