David Widmann
David Widmann
This came up in https://github.com/JuliaGaussianProcesses/KernelFunctions.jl/pull/386: Sometimes you might want to use `grad=false`, e.g., if there is neither a rule in DiffRule nor do you want to use ForwardDiff. However, in...
Currently, MCMCChains does not handle weighted samples such as the ones output by IS correctly and reports meaningless statistics. This came up in https://github.com/TuringLang/Turing.jl/issues/1467. Maybe one approach could be to...
The following code fails for the in-place method but works for the out-of-place version: ```julia using OrdinaryDiffEq, Unitful f_oop(y,p,t) = 0.5*y / 3.0u"s" f_ip(dy,y,p,t) = (dy .= 0.5.*y ./ 3.0u"s")...
Similar to what I speculated about in https://github.com/JuliaDiffEq/DiffEqBase.jl/pull/318#issuecomment-521131006. Actually I think probably one should not use generated functions since the function body would have to be generated for every different...
I just noticed that it's a bit weird that we compute `u0` when a problem is created for which only `h` is specified. IMO it's more reasonable to just assign...
It seems as soon as the concrete values of `u0`, `tspan`, and `p` are known, we can infer whether the provided model is in in-place or out-of-place form (or if...
I'm currently copying the implementation of the Anderson acceleration (unfortunately, we really should only have one implementation...) but I'm confused about how the norm of the residuals is updated. https://github.com/JuliaDiffEq/DiffEqBase.jl/blob/ed5bec25f95b1ef349307a6a2ee3468c485d283c/src/nlsolve/functional.jl#L159...
The default implementation for DDE problems in lines https://github.com/JuliaDiffEq/DiffEqBase.jl/blob/master/src/diffeqfunction.jl#L536-L542 (and similar below) does not make sense since for DDE problems the Jacobian does also depend on `h`. However, I guess...
In line with https://github.com/JuliaDiffEq/DelayDiffEq.jl/pull/130, we should also fix all closures in DiffEqBase (and possibly other JuliaDiffEq packages) that access closed over variables since apparently it's behaviour is undefined. This issue...
This PR fixes https://github.com/JuliaStats/Distances.jl/issues/238 and deprecates `pairwise!(r, dist, a[, b])` in favour of `pairwise!(dist, r, a[, b])` which is consistent with StatsAPI and StatsBase. It seemed natural to keep `colwise!`...