no code implementations • 31 May 2016 • Yura N. Perov
In Chapter 3, we describe a way to facilitate sequential Monte Carlo inference in probabilistic programming using data-driven proposals.
no code implementations • 26 Jan 2016 • Yura N. Perov
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.
no code implementations • 14 Dec 2015 • Yura N. Perov, Tuan Anh Le, Frank Wood
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.
no code implementations • 9 Jul 2014 • Yura N. Perov, Frank D. Wood
We develop a technique for generalising from data in which models are samplers represented as program text.
no code implementations • NeurIPS 2013 • Vikash K. Mansinghka, Tejas D. Kulkarni, Yura N. Perov, Joshua B. Tenenbaum
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.