sanmitraghosh

Lets say we have an ODE dX/dt = F(X,p) Now for calculating sensitivity we need the Jacobians dF/dp and dF/dX. Now we can calculate these quantities fast enough using sympy's...

I will see if I can write a PINTS example with this technique in the context of MALA/HMC

@martinjrobins Sympy Lamdify is faster compared to PyTorch's autograd when used for calculating the Jacobians at each time step/solver call. Both running on CPU. But it would be good to...

@martinjrobins Jax should be best because it is the only AD software where you can mix forward mode and reverse mode. So one can use forward for Jacobians and reverse...

By the way @martinjrobins I have got a grad student working on changing all my current numpy stuff to Jax ! :)

@martinjrobins Great news !

I think you need not worry about Gaussian kernel, matern is much more useful in practical applications.

I kind of find both these not very useful. I would rather prefer beta*log p(x|theta) + log p(theta). This is the thermodynamic integral path and thus naturally gives me the...

@ben18785 @MichaelClerx I want to quickly write down my final observations regarding this issue. 1) I am happy as long as the tempering is either a) beta * log(theta|x) +...