Applications of Probabilistic Programming (Master's thesis, 2015)

Understand

This thesis describes work on two applications of probabilistic programming: the learning of probabilistic program code given specifications, in particular program code of one-dimensional samplers; and the facilitation of sequential Monte Carlo inference with help of data-driven proposals.

• The latter is presented with experimental results on a linear Gaussian model and a non-parametric dependent Dirichlet process mixture of objects model for object recognition and tracking.
• In Chapter 1 we provide a brief introduction to probabilistic programming.
• In Chapter 2 we present an approach to automatic discovery of samplers in the form of probabilistic programs.