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Using `truncated` with `±Inf` as the bounds tends to lead to NaN's when using automatic differentiation:
```julia
julia> using Distributions; f(s) = logpdf(truncated(Normal(0.0, s[1]), 0, +Inf), 2…
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Automatic differentiation would be useful so we don't have to implement linearizations or Jacobians of complicated analytic functions. Examples where autodiff would help:
- Jacobians and Hessians o…
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## Feature request
It would be great if Numba supported automatic differentiation. Maybe using [Enzyme](https://github.com/EnzymeAD/Enzyme) would be the easiest way as it operates directly on th…
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[Zygote](https://github.com/FluxML/Zygote.jl) is the most used Automatic Differentiation tool and it doesn't support static arrays due to their use for mutations [^1]. `DCM` and `Quaternion` structure…
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This issue is created to keep track of progress and responsibilities for the Clad documentation hackathon.
The following concepts along with their person-in-charge are as follows:
'?' is used if…
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https://gist.github.com/rxwei/30ba75ce092ab3b0dce4bde1fc2c9f1d
@jrevels this is a very very interesting read that has some potential ideas for Julia's AD ecosystem.
@jekbradbury knows more.
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`Jet` provides the basic machinery for forward mode AD, but the supporting apis and machinery are largely missing.
It would be useful to have robust apis available for actual production usage.
This…
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I notice interesting studies in the iminuit package. Just dropping the links here, might be linked somewhere in README, or used for an inspiration when writing future benchmarks.
- RooFit vs numba…
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Listing here some ideas for useful things to have examples for, anyone feel free to add
- [ ] How to access/use automatic differentiation derivative information (say, if you loaded in a DESC eq to…