# Nicholas Kinar

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 Oct12 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? Oct11 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? Oct11 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. Oct11 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? Oct11 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. Oct11 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. Oct11 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). Aug2 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. Aug2 comment Calculating and comparing histograms of complex numbers @whuber: Thanks for putting the complex numbers in the same framework as multidimensional goodness of fit. Is there anything else that is done differently with the complex numbers? I would assume that the multidimensional $\chi^2$ test would also be applied to complex numbers as well. May30 comment Conceptual understanding of root mean squared error and mean bias deviation OK, thanks Michael, this makes sense May30 comment Conceptual understanding of root mean squared error and mean bias deviation Ah - okay, this is making sense to me now. So if the RMSE tells us how good the model is, then what would be the purpose of looking at both the RMSE and the MBD? May29 comment Conceptual understanding of root mean squared error and mean bias deviation Thanks again, Michael. So a high RMSE and a low MBD implies that it is a good model? May29 comment Conceptual understanding of root mean squared error and mean bias deviation @whuber: Thanks whuber!. I've looked around the site, but to me I am still finding it a bit challenging to understand what is really meant in the context of my own research. May29 comment Conceptual understanding of root mean squared error and mean bias deviation Thank you; this is very much appreciated. I am still finding it a little bit challenging to understand what is the difference between RMSE and MBD. As I understand it, RMSE quantifies how close a model is to experimental data, but what is the role of MBD? Maybe my misunderstanding is just associated with terminology.