46
edits
Ozzy
Joined 25 March 2015
→Y: Your task
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<math> y \approx Dx</math> | <math> y \approx Dx</math> | ||
where each of the coordinates of {{codeline|y}} lies within {{codeline|alpha}} confidence interval (normal distributed error assumed). Out of all possible {{codeline|x}} the one with the highest entropy is chosen. {{codeline|info}} describes the convergence of the | where each of the coordinates of {{codeline|y}} lies within {{codeline|alpha}} confidence interval (normal distributed error assumed). Out of all possible {{codeline|x}} the one with the highest entropy is chosen. {{codeline|info}} describes the convergence of the algorithm. The other returned parameters will describe final gradients, Hessians and Lagrange's coefficient. | ||
* another version would be defined for a non-linear function. The declaration would very similar | * another version would be defined for a non-linear function. The declaration would very similar | ||
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<math> y \approx f(x)</math> | <math> y \approx f(x)</math> | ||
It is | It is convenient to have this version of the algorithm for problem where obtaining the transformation matrix is difficult to compute or affects performance (think fft). The algorithm is expected to give good results for linear functions. For not-too-complicated non-linear cases the chances are still there. | ||
Additional work will be put to provide some wrapper functions to allow the user quickly use MEM in their problem. This includes function for 1D and image deconvolutions, time series components analysis, power spectral estimation and other applications I will be able to find in Matlab or other computational software. | Additional work will be put to provide some wrapper functions to allow the user quickly use MEM in their problem. This includes function for 1D and image deconvolutions, time series components analysis, power spectral estimation and other applications I will be able to find in Matlab or other computational software. | ||