This came up in a conversation with @seantalts . When solving an algebraic equation, we find y^* such that f(y^*, \theta) = 0 where \theta are our model parameters. We compute the jacobian matrix of y with respect to \theta using the implicit function theorem: J = - [J^y(y^*, \theta)]^{-1} J^\thet…

Better computation of the Jacobian matrix for algebraic solver

charlesm93 June 4, 2019, 12:28pm 9

Alright, let’s give it a try.

Scalable Bayesian multilevel modeling