Nicholas Kinar

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	140 reputation	bio	website		visits	member for	11 months
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Feb 24	awarded	Notable Question
Oct 31	awarded	Popular Question
Oct 16	accepted	Separating two complex-valued datasets that have been multiplied together
Oct 12	comment	Separating two complex-valued datasets that have been multiplied together Thanks for your insightful comments. I am not looking for a single answer for A or B, I am only looking for a numerical procedure that works well enough to get a good approximation. I might be able to get the distribution of B, and so the Monte Carlo approach seems to be useful. Could you suggest a reference?
Oct 11	comment	Separating two complex-valued datasets that have been multiplied together Yes, I think this is a deconvolution problem, but I only know the parametric equation form of A and perhaps the statistical distribution of B. This might be a blind deconvolution problem, but up to now navigating the literature has been tricky, so I am thinking that there might be a statistical method to do the same in a similar way. If there is a blind deconvolution algorithm, where might I look to be able to implement it?
Oct 11	comment	Separating two complex-valued datasets that have been multiplied together Thanks, Bitwise. What constraints are required (statistical or otherwise) to make this ill-posed problem into one that is tractable? And what is a good algorithm to do the reconstruction? I am finding it a bit challenging to navigate the literature, and I need a suggestion of what procedure I should use, and a good reference on the implementation.
Oct 11	comment	Separating two complex-valued datasets that have been multiplied together OK, I have updated my question above. Does this give more information on how to set the problem up?
Oct 11	revised	Separating two complex-valued datasets that have been multiplied together added more clarifications
Oct 11	awarded	Commentator
Oct 11	comment	Separating two complex-valued datasets that have been multiplied together Thanks, whuber; what additional information do I need or is required to allow for the proposal of these changes? What is a good measure of the roughness of $A$ or $B$? I will update my question above. Is there an example (i.e. tutorial, paper or book) that demonstrates how these constraints can be applied? Please ask if anyone requires additional information.
Oct 11	revised	Separating two complex-valued datasets that have been multiplied together added more information
Oct 11	comment	Separating two complex-valued datasets that have been multiplied together Thanks again, Bitwise. How might I set up the numerical algorithm to enforce a certain distribution? I am not seeking perfection here (that is the domain of exact mathematics); I am only looking for a method to "approximately" separate A and B using some sort of statistical information or method.
Oct 11	revised	Separating two complex-valued datasets that have been multiplied together Added some information
Oct 11	comment	Separating two complex-valued datasets that have been multiplied together Thanks for your response, Bitwise. Given additional constraints (i.e. distribution of the datasets), I would wonder if A and B might be approximated in some way. What if B does not equal C, and A does not equal 1 at all coordinates? Both A and B can be said to have a statistical distribution (but at this time, I do not know the distributions).
Oct 11	asked	Separating two complex-valued datasets that have been multiplied together
Aug 15	asked	Estimating parameter using curve-fitting and model comprised of uncorrelated product of two functions
Aug 2	revised	Are there methods for automatically detecting features of a curve? Added more useful information
Aug 2	revised	Are there methods for automatically detecting features of a curve? added 292 characters in body
Aug 2	answered	Are there methods for automatically detecting features of a curve?
Aug 2	comment	Calculating and comparing histograms of complex numbers @whuber: Actually, my application is digital signal processing (DSP), where a filter kernel convolution is applied in the frequency domain by the multiplication of the complex number representing the filter kernel at a discrete frequency with the complex number of the frequency domain signal at a discrete frequency. I've never heard of directional statistics; that's a new term for me, and it is very neat. Thanks for posting this.