Mixture models monte carlo bayesian updating and dynamic models scared to start dating again

Tensorflow is driving me nuts - once you've used Py Torch it's painful to go back to TF!Particle filters (PFs) are powerful sampling-based inference/learning algorithms for dynamic Bayesian networks (DBNs).I think principled Bayesian computation, overcomes many of the deficiencies of deep learning, and vice versa.Check out Shakir's NIPs 2016 slides Hey @smth, please post here/let me know, when Uber or anyone else make public some sort of Black-Box variational inference engine public for Py Torch.

We first review R packages that provide Bayesian estimation tools for a wide range of models.

We show that Rao-Blackwellised particle filters (RBPFs) lead to more accurate estimates than standard PFs.

We demonstrate RBPFs on two problems, namely non-stationary online regression with radial basis function networks and robot localization and map building.

We then discuss packages that address specific Bayesian models or specialized methods in Bayesian statistics.

This is followed by a description of packages used for post-estimation analysis.