TransformVariables: a subset of optics functionality? about transformvariables.jl HOT 7 CLOSED

I consider calculating the log determinant of the Jacobian major functionality, and I am not sure how you would implement that with optics. See transform_and_logjac.

Also, a lot of transformations are nonlinear mappings.

Are you sure you read the manual?

from transformvariables.jl.

Also, a lot of transformations are nonlinear mappings.

See logs in my example, nonlinear functions work just fine.

calculating the log determinant of the Jacobian

Thanks for pointing this out! Not sure if I totally understand what exactly is meant by Jacobian here. It's commonly defined for vector-to-vector functions, but what's a Jacobian of NamedTuple function as((μ = asℝ, σ = asℝ₊, τ = asℝ₊, θs = as(Array, 8)))?

Optics can easily be differentiated, I regularly compute gradients with them. But don't understand what you mean by Jacobian.

from transformvariables.jl.

See https://en.wikipedia.org/wiki/Jacobian_matrix_and_determinant#Jacobian_determinant, https://en.wikipedia.org/wiki/Integration_by_substitution#Substitution_for_multiple_variables, and the docs.

from transformvariables.jl.

Yes, that's the definition of log-Jacobian for functions R^n -> R^n (vector -> vector). But what's a Jacobian of a vector -> namedtuple function?

from transformvariables.jl.

The Jacobian of the result flattened to a vector. (Edit: for non-bijective transforms, a subset, eg the upper triangle of correlation matrices).

from transformvariables.jl.

I see... This very specific functionality is not available out of the box in Accessors. One can use regular autodiff there to find gradients/jacobians/..., but need to specify both input(s) and output(s) of the differentiated function instead of them being implicit.
Sorry for bothering with the suggestion then. I made this issue just because earlier at some point I used TransformVariables for transform/inverse, and now optics replaced that functionality for me.

from transformvariables.jl.

No worries, suggestions are always welcome. Yes, this functionality is very specific to Bayesian estimation / MCMC. I will close the issue for now as I think it is resolved, but if you comment I will reopen it.

from transformvariables.jl.