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I can not understand the deduction process of Maximum likelihood under Chapter 5. Loss functions. Is there any math materials can help me to understand it better?
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Could you the synthetic control combined with the Poisson pseudo-maximum-likelihood (PPML) estimator? And do you know of any example of papers that uses this approach?
For context: I wish to run a …
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right now $\hat{\theta}$ for the `scLANE` and null models is being estimated using the method of moments (MM). however, this requires the estimation of the degrees-of-freedom, which I'm not sure is be…
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I mentioned it in several issues but don't see a specific issue for it
see also #3273 and #3284
This is the MLE version of least trimmed squares LTS or ELTS.
PR #452 contains a draft version `…
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Hi All,
I have question, does the linear regression in jamovi have maximum likelihood in its settings or background to deal with missing data? just like the path analysis and factor analysis opti…
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It will be useful to have an additional `loss` module and implement the MSE loss and the complex maximum likelihood loss.
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https://adamhu.github.io/2019/06/Maximum-Likelihood-Estimation/
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MLTomo requires the observables to be POVMS. If we're [hacking in Hermeticity,](https://github.com/BBN-Q/Qlab.jl/blob/master/src/tomography.jl#L125), we should try to do the same for other requiremen…
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@mbakker7, is it an idea to move the LmFitSolverNew to a separate branch or remove it?