LogDensityProblems.jl
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tpapp
A common framework for implementing and using log densities for inference.
Some recent MCMC samplers such as ["Randomize-then-optimize"](https://epubs.siam.org/doi/10.1137/140964023) 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...
It is time for a redesign of the stress testing framework. Specifically, 1. **Restrict input range**. Using Cauchy for random coordinates is excessive, as it can easily result in values...
DynamicHMC does not deal well with incorrect deriatives. These can result from AD errors (conceptual errors in mature AD libraries are rare, but numerical corner cases can still happen), or...
When writing this package I implicitly assumed that that whenever the log density is finite, the gradient (and now the Hessian, see #101) are also. So calling eg `logdensity_and_gradient` in...
# Motivation Suppose that for a set of parameters $x$, the equation $F(x, y) = 0$ defines $y(x)$ implicitly. Eg $x$ could be parameters to a problem that we approximate...