Timeline for Estimate peak width from a vector that is a superposition of unknown number of identical Gaussian peaks with different heights?
Current License: CC BY-SA 4.0
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| Feb 11, 2022 at 11:53 | history | edited | Tom Wenseleers | CC BY-SA 4.0 |
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| Aug 20, 2019 at 11:26 | comment | added | Tom Wenseleers |
One can also use Poisson 1/variance weights btw in the nnls() step, using weights = 1/(y+1); nnls(A=bandedM*sqrt(weights), b=y*sqrt(weights)) - that would better take into account the fact that we have Poisson noise here. [It would become similar to Richardson-Lucy deconvolution then, but would then use a closed-form single step weighted least squares approximation; if need be the variance weights could also be further updated based on the fitted values using an IRLS algorithm, like the ones used to solve generalized linear models, as in bwlewis.github.io/GLM/]
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| Aug 20, 2019 at 11:20 | comment | added | Tom Wenseleers | Well it's quite easy really - just install R and RStudio, and step through it line by line using CTRL-ENTER so that you can see what each line does :-) | |
| Aug 20, 2019 at 4:52 | comment | added | Royi | I wish the code was MATLAB so I can understand it :-). | |
| S Jul 2, 2019 at 21:44 | history | suggested | Glorfindel | CC BY-SA 4.0 |
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| Jul 2, 2019 at 18:31 | review | Suggested edits | |||
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| Jan 26, 2019 at 12:25 | history | edited | Tom Wenseleers | CC BY-SA 4.0 |
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| Jan 14, 2019 at 14:48 | comment | added | A_A | I think that it would be worth updating the question with more information about what you were trying to achieve (e.g. your comment towards Cedron Dawg) and how the solution you provided fits that particular use case. If you added a few lines in the intro of your Q and the intro of your A, that would make this a very valuable post in general. All the best. | |
| Jan 12, 2019 at 15:39 | history | edited | Tom Wenseleers | CC BY-SA 4.0 |
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| Jan 12, 2019 at 14:10 | history | edited | Tom Wenseleers | CC BY-SA 4.0 |
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| Jan 11, 2019 at 19:28 | history | edited | Peter K.♦ | CC BY-SA 4.0 |
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| Jan 11, 2019 at 16:42 | history | edited | Tom Wenseleers | CC BY-SA 4.0 |
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| Jan 10, 2019 at 22:51 | history | edited | Tom Wenseleers | CC BY-SA 4.0 |
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| Jan 10, 2019 at 22:00 | history | edited | Tom Wenseleers | CC BY-SA 4.0 |
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| Jan 10, 2019 at 21:54 | history | edited | Tom Wenseleers | CC BY-SA 4.0 |
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| Jan 10, 2019 at 21:18 | history | edited | Tom Wenseleers | CC BY-SA 4.0 |
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| Jan 10, 2019 at 21:05 | comment | added | Peter K.♦ | Looks pretty good to me! Nicely done. | |
| Jan 10, 2019 at 20:25 | history | edited | Tom Wenseleers | CC BY-SA 4.0 |
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| Jan 10, 2019 at 20:17 | history | edited | Tom Wenseleers | CC BY-SA 4.0 |
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| Jan 10, 2019 at 20:01 | history | edited | Tom Wenseleers | CC BY-SA 4.0 |
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| Jan 10, 2019 at 19:50 | history | edited | Tom Wenseleers | CC BY-SA 4.0 |
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| Jan 10, 2019 at 19:30 | history | answered | Tom Wenseleers | CC BY-SA 4.0 |