Search Results for author:
Found 8 papers, 3 papers with code
Bayesian learning of the optimal action-value function in a Markov decision process
We provide a full Bayesian framework, from modelling to inference to decision-making.
Bayesian Deep Learning with Multilevel Trace-class Neural Networks
In particular, an MLMC method that was introduced is used to approximate posterior expectations of Bayesian TNN models with optimal computational complexity, and this is mathematically proved.
Trace-class Gaussian priors for Bayesian learning of neural networks with MCMC
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.
Online Particle Smoothing with Application to Map-matching
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
Coupled conditional backward sampling particle filter
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
Tracking multiple moving objects in images using Markov Chain Monte Carlo
A new Bayesian state and parameter learning algorithm for multiple target tracking (MTT) models with image observations is proposed.
Bayesian tracking and parameter learning for non-linear multiple target tracking models
We propose a new Bayesian tracking and parameter learning algorithm for non-linear non-Gaussian multiple target tracking (MTT) models.
An Online Expectation-Maximisation Algorithm for Nonnegative Matrix Factorisation Models
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.
