43 citations · 78 across the 6 of their papers we have counts for
6 papers
A State-Space Perspective on Modelling and Inference for Online Skill Rating
Samuel Duffield, Samuel Power, Lorenzo Rimella
We summarise popular methods used for skill rating in competitive sports, along with their inferential paradigms and introduce new approaches based on sequential Monte Carlo and di…
Demonstrating Bayesian Quantum Phase Estimation with Quantum Error Detection
Kentaro Yamamoto, Samuel Duffield, Yuta Kikuchi +1
Quantum phase estimation (QPE) serves as a building block of many different quantum algorithms and finds important applications in computational chemistry problems. Despite the rap…
Quasi-Newton Sequential Monte Carlo
Samuel Duffield, Sumeetpal S. Singh
Sequential Monte Carlo samplers represent a compelling approach to posterior inference in Bayesian models, due to being parallelisable and providing an unbiased estimate of the pos…
Bayesian Learning of Parameterised Quantum Circuits
Samuel Duffield, Marcello Benedetti, Matthias Rosenkranz
Currently available quantum computers suffer from constraints including hardware noise and a limited number of qubits. As such, variational quantum algorithms that utilise a classi…
Ensemble Kalman Inversion for General Likelihoods
Samuel Duffield, Sumeetpal S. Singh
In this letter we generalise Ensemble Kalman inversion techniques to general Bayesian models where previously they were restricted to additive Gaussian likelihoods - all in the dif…
Online Particle Smoothing with Application to Map-matching
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. Unlike classical…