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Feature request: Jacobian calculation for gradients #36
I'd like to use TransformVariables as part of an HMC sampler, but my evaluating my target distribution's probability density involves solving a partial differential equation, which is done outside Julia and thus not compatible with AutoDiff. There are a special techniques for obtaining gradients for such models which involve solving an adjoint equations, but that's a digression.
For people with use cases like mine, it would be nice if there was a function which for a transformation
I am not entirely sure what you are asking for here. You have a transformation, you can also calculate its log Jacobian determinant, and you would like to use it for Bayesian inference?
Sorry, I am not getting this --- the Jacobian is for the whole transformation, not by coordinate.
Perhaps a mock example would help.
Suppose we have two random variables,
I am interested in
Right now, the examples in DynamicHMC do this by applying automatic differentiation to
Does this example help?