Data-driven Sequential Monte Carlo in Probabilistic Programming

By Yura N. Perov, Tuan Anh Le, Frank D. Wood. 2015

7 citations recorded by the source corpus.

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. In this work, we describe an approach for training a discriminative model, namely a neural network, in order to approximate the optimal proposal by using posterior estimates from previous runs of inference. We show an example that incorporates a data-driven proposal for use in a non-parametric model in the Anglican probabilistic programming system. Our results show that data-driven proposals can significantly improve inference performance so that considerably fewer particles are necessary to perform a good posterior estimation.

Subfields: Artificial Intelligence, Probabilistic & Bayesian Methods

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Saurabh Johri
2. Ciarán M. Lee
3. Jonathan G. Richens
4. Adam Baker
5. Kostis Gourgoulias
6. Logan Graham
7. Yura N. Perov

In Probabilistic & Bayesian Methods

1. Adam Baker
2. Saurabh Johri
3. Kostis Gourgoulias
4. Ciarán M. Lee
5. Logan Graham
6. Jonathan G. Richens
7. Yura N. Perov
8. Frank D. Wood

Citations in the corpus

• MultiVerse: Causal Reasoning using Importance Sampling in Probabilistic Programming (2019, methodology): “The standard inference techniques for amortised approximate inference (Gu et al., 2015; Germain et al., 2015; Perov et al., 2015; Paige and Wood, 2016a; Perov, 2016; Le et al., 2016; Morris, 2001; Paige and Wood, 2016b; Ritchie et al., 2016; Le et al., 2017) and in particular importance sampling inf”