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Applications of Probabilistic Programming (Master's thesis, 2015)
In Chapter 3, we describe a way to facilitate sequential Monte Carlo inference in probabilistic programming using data-driven proposals.
Bachelor's thesis on generative probabilistic programming (in Russian language, June 2014)
This Bachelor's thesis, written in Russian, is devoted to a relatively new direction in the field of machine learning and artificial intelligence, namely probabilistic programming.
Data-driven Sequential Monte Carlo in Probabilistic Programming
Most of Markov Chain Monte Carlo (MCMC) and sequential Monte Carlo (SMC) algorithms in existing probabilistic programming systems suboptimally use only model priors as proposal distributions.
Learning Probabilistic Programs
We develop a technique for generalising from data in which models are samplers represented as program text.
Approximate Bayesian Image Interpretation using Generative Probabilistic Graphics Programs
The idea of computer vision as the Bayesian inverse problem to computer graphics has a long history and an appealing elegance, but it has proved difficult to directly implement.
