Fit and compare models very quickly. MCMC-free.

Posterior distributions and corner plots without MCMC? No dealing with convergence criteria?

Yes!

Tailored for low-dimensional (d<10) problems with a single mode, this package automatically finds the best fit and uses the local covariance matrix as a Laplace Approximation. Then Importance Sampling and Variational Bayes come in to improve from a single-gaussian approximation to more complex shapes. This allows sampling efficiently in some problems, and provides a estimate for the marginal likelihood.

This package is built on top the excellent (i)minuit and pypmc packages.