Posts by Fabrizio Benedetti

1) Message boards : SETI@home Science : Kerned density estimation instead of histograms (Message 982187) Posted 21 Mar 2010 by Fabrizio Benedetti Post: How is that more useful for the signal analysis than the present code? (The present bins are assumed to be narrow enough to find any wanted gaussians.) Does the KDE discriminate better than fixed histogram bins to more accurately find gaussians that are noisy and still reject random noise that might look like a noisy gaussian? Is the KDE easy to compute? Thanks, could be interesting?... Regards, Martin Unfortunately I cannot reply to all of your question here, also because it is necessary a lot of math (and some steps are still unclear also for me). What can I do is start to partially reply to your first question, for that I suggest to see the example that is show here: http://school.maths.uwa.edu.au/~duongt/seminars/intro2kde/ and still reject random noise that might look like a noisy gaussian? Does an histogram do that? In general the calculation of the KDE is more intensive but more accurate and approximate better the true probability density function (look here for more example and discussion: www-hermes.desy.de/notes/pub/TALK/sgliske.tpsh09.pdf ). An interest example wrote in python could be found here: http://jpktd.blogspot.com/2009/03/using-gaussian-kernel-density.html it is really similar to the code that i use to show and fit my data.
2) Message boards : SETI@home Science : Kerned density estimation instead of histograms (Message 974525) Posted 27 Feb 2010 by Fabrizio Benedetti Post: Hello I have a question (or a suggestion) for you. In the window of seti software it is possible to see an histogram. This is for the search of a particular signal with gaussian shape (I suppose...). Why you don't use the Kernel Density Estimation (KDE) to do that? The KDE is a powerful statitical method similar to the histogram but: - it doesn't need the bins and the bin size - it doesn't have the bin position problem (I mean where you have to start your binning) - it converge fastly than a histogram to the real probability density function Tnx for the attention and sorry for the poor english. Bye
3) Message boards : SETI@home Science : Wavelet trasform (Message 669675) Posted 30 Oct 2007 by Fabrizio Benedetti Post: Wavelet trasform are better than FFTW. They are similar to a global FFTW without the problem of the "window". You can look here : http://users.rowan.edu/~polikar/RESEARCH/PUBLICATIONS/iasted02.pdf You can find wavelet code (and a good explanation) in "Numerical recipes in C++"
4) Message boards : SETI@home Science : Wavelet trasform (Message 667729) Posted 27 Oct 2007 by Fabrizio Benedetti Post: Hi to all. I have a question. Why seti@home is continuing to use the "FFT"? The FFT are "n*log2(n)" order. Wavelet trasform are "n" order and they are better than the "FFT". This is what i have study but i can be wrong. (Sorry for bad english it's not my language).

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