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Some recent MCMC samplers such as "Randomize-then-optimize" exploit least squares structure in the log-likelihood; i.e., the log-density has the form $f(x) = \sum_j f_j(x)^2$. Could we add this kind of derivative information into the API? As a proposal, I would suggest something like
residual(ℓ, x)
jacobian(ℓ, x)
which is similar to the NLSolversBase.jl API. Non-allocating versions would be helpful as well, e.g., writing into the last argument
residual!(ℓ, x, res)
jacobian!(ℓ, x, jac)
The text was updated successfully, but these errors were encountered:
Sorry for the late reply. I am not entirely sure that this package is the ideal home for an API like that, for the following reasons:
it is pretty much orthogonal to our current one
either we require that all implementations support it, or introduce further API to query support
I would recommend that a package using that algorithm just implements this API, and then if multiple methods end up using it it can be factored out to an interface package.
Some recent MCMC samplers such as "Randomize-then-optimize" exploit least squares structure in the log-likelihood; i.e., the log-density has the form$f(x) = \sum_j f_j(x)^2$ . Could we add this kind of derivative information into the API? As a proposal, I would suggest something like
which is similar to the NLSolversBase.jl API. Non-allocating versions would be helpful as well, e.g., writing into the last argument
The text was updated successfully, but these errors were encountered: