no code implementations • 24 Mar 2022 • Neil K. Chada, Ajay Jasra, Kody J. H. Law, Sumeetpal S. Singh
In this article we consider Bayesian inference associated to deep neural networks (DNNs) and in particular, trace-class neural network (TNN) priors which were proposed by Sell et al. [39].
1 code implementation • 20 Dec 2020 • Torben Sell, Sumeetpal S. Singh
We also implement examples in Bayesian Reinforcement Learning to automate tasks from data and demonstrate, for the first time, stability of MCMC to mesh refinement for these type of problems.
1 code implementation • 8 Dec 2020 • Samuel Duffield, Sumeetpal S. Singh
We introduce a novel method for online smoothing in state-space models that utilises a fixed-lag approximation to overcome the well known issue of path degeneracy.
Methodology Applications
1 code implementation • 15 Jun 2018 • Anthony Lee, Sumeetpal S. Singh, Matti Vihola
This complements the earlier findings in the literature for conditional particle filters, which assume the number of particles to grow (super)linearly in terms of the time horizon.
Computation Probability Primary 65C05, secondary 60J05, 65C35, 65C40
no code implementations • 17 Mar 2016 • Lan Jiang, Sumeetpal S. Singh
A new Bayesian state and parameter learning algorithm for multiple target tracking (MTT) models with image observations is proposed.
no code implementations • 8 Oct 2014 • Lan Jiang, Sumeetpal S. Singh, Sinan Yildirim
We propose a new Bayesian tracking and parameter learning algorithm for non-linear non-Gaussian multiple target tracking (MTT) models.
no code implementations • 11 Jan 2014 • Sinan Yildirim, A. Taylan Cemgil, Sumeetpal S. Singh
In this paper we formulate the nonnegative matrix factorisation (NMF) problem as a maximum likelihood estimation problem for hidden Markov models and propose online expectation-maximisation (EM) algorithms to estimate the NMF and the other unknown static parameters.