Simon Maskell
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View article: ML-ABC: Machine-learning assisted Approximate Bayesian Computation for efficient calibration of agent-based models for pandemic outbreak analysis
ML-ABC: Machine-learning assisted Approximate Bayesian Computation for efficient calibration of agent-based models for pandemic outbreak analysis Open
Mathematical modelling with agent-based models (ABMs) has gained popularity during the COVID-19 pandemic, but their complexity makes efficient and robust calibration to data challenging, particularly when applying Bayesian methods to quant…
View article: ML-ABC: Machine-learning assisted Approximate Bayesian Computation for efficient calibration of agent-based models for pandemic outbreak analysis
ML-ABC: Machine-learning assisted Approximate Bayesian Computation for efficient calibration of agent-based models for pandemic outbreak analysis Open
Mathematical modelling with agent-based models (ABMs) has gained popularity during the COVID-19 pandemic, but their complexity makes efficient and robust calibration to data challenging, particularly when applying Bayesian methods to quant…
View article: Towards Scalable Proteomics: Opportunistic SMC Samplers on HTCondor
Towards Scalable Proteomics: Opportunistic SMC Samplers on HTCondor Open
Quantitative proteomics plays a central role in uncovering regulatory mechanisms, identifying disease biomarkers, and guiding the development of precision therapies. These insights are often obtained through complex Bayesian models, whose …
View article: Hess-MC2: Sequential Monte Carlo Squared using Hessian Information and Second Order Proposals
Hess-MC2: Sequential Monte Carlo Squared using Hessian Information and Second Order Proposals Open
When performing Bayesian inference using Sequential Monte Carlo (SMC) methods, two considerations arise: the accuracy of the posterior approximation and computational efficiency. To address computational demands, Sequential Monte Carlo Squ…
View article: Assessing the Impact of Vaccination on Rotavirus Transmission Dynamics Using Bayesian Inference
Assessing the Impact of Vaccination on Rotavirus Transmission Dynamics Using Bayesian Inference Open
The introduction of the rotavirus vaccine in the United Kingdom (UK) in 2013 led to a noticeable decline in laboratory reports in subsequent years. To assess the impact of vaccination on rotavirus transmissibility we calibrated a stochasti…
View article: Developing an AI-Assisted Tool That Identifies Patients With Multimorbidity and Complex Polypharmacy to Improve the Process of Medication Reviews: Qualitative Interview and Focus Group Study
Developing an AI-Assisted Tool That Identifies Patients With Multimorbidity and Complex Polypharmacy to Improve the Process of Medication Reviews: Qualitative Interview and Focus Group Study Open
Background Structured medication reviews (SMRs) are an essential component of medication optimization, especially for patients with multimorbidity and polypharmacy. However, the process remains challenging due to the complexities of patien…
View article: Humble your Overconfident Networks: Unlearning Overfitting via Sequential Monte Carlo Tempered Deep Ensembles
Humble your Overconfident Networks: Unlearning Overfitting via Sequential Monte Carlo Tempered Deep Ensembles Open
Sequential Monte Carlo (SMC) methods offer a principled approach to Bayesian uncertainty quantification but are traditionally limited by the need for full-batch gradient evaluations. We introduce a scalable variant by incorporating Stochas…
View article: Efficient MCMC Sampling with Expensive-to-Compute and Irregular Likelihoods
Efficient MCMC Sampling with Expensive-to-Compute and Irregular Likelihoods Open
Bayesian inference with Markov Chain Monte Carlo (MCMC) is challenging when the likelihood function is irregular and expensive to compute. We explore several sampling algorithms that make use of subset evaluations to reduce computational o…
View article: Utilising Gradient-Based Proposals Within Sequential Monte Carlo Samplers for Training of Partial Bayesian Neural Networks
Utilising Gradient-Based Proposals Within Sequential Monte Carlo Samplers for Training of Partial Bayesian Neural Networks Open
Partial Bayesian neural networks (pBNNs) have been shown to perform competitively with fully Bayesian neural networks while only having a subset of the parameters be stochastic. Using sequential Monte Carlo (SMC) samplers as the inference …
View article: Poisson multi-Bernoulli mixture filter for trajectory measurements
Poisson multi-Bernoulli mixture filter for trajectory measurements Open
This paper presents a Poisson multi-Bernoulli mixture (PMBM) filter for multi-target filtering based on sensor measurements that are sets of trajectories in the last two-time step window. The proposed filter, the trajectory measurement PMB…
View article: Incorporating the ChEES Criterion into Sequential Monte Carlo Samplers
Incorporating the ChEES Criterion into Sequential Monte Carlo Samplers Open
Markov chain Monte Carlo (MCMC) methods are a powerful but computationally expensive way of performing non-parametric Bayesian inference. MCMC proposals which utilise gradients, such as Hamiltonian Monte Carlo (HMC), can better explore the…
View article: Identifying Behaviours Indicative of Illegal Fishing Activities in Automatic Identification System Data
Identifying Behaviours Indicative of Illegal Fishing Activities in Automatic Identification System Data Open
Identifying illegal fishing activities from Automatic Identification System (AIS) data is difficult since AIS messages are broadcast cooperatively, the ship’s master controls the timing, and the content of the transmission and the activiti…
View article: An Entropic Metric for Measuring Calibration of Machine Learning Models
An Entropic Metric for Measuring Calibration of Machine Learning Models Open
Understanding the confidence with which a machine learning model classifies an input datum is an important, and perhaps under-investigated, concept. In this paper, we propose a new calibration metric, the Entropic Calibration Difference (E…
View article: A Massively Parallel SMC Sampler for Decision Trees
A Massively Parallel SMC Sampler for Decision Trees Open
Bayesian approaches to decision trees (DTs) using Markov Chain Monte Carlo (MCMC) samplers have recently demonstrated state-of-the-art accuracy performance when it comes to training DTs to solve classification problems. Despite the competi…
View article: Attitudes towards deprescribing in patients with multimorbidity and polypharmacy in primary care
Attitudes towards deprescribing in patients with multimorbidity and polypharmacy in primary care Open
Background Population ageing has led to an increase in multimorbidity and polypharmacy. Some medications may need to be stopped, but patient attitudes towards deprescribing are poorly understood. This study explores attitudes towards (de)p…
View article: Fully Bayesian Wideband Direction-of-Arrival Estimation and Detection via RJMCMC
Fully Bayesian Wideband Direction-of-Arrival Estimation and Detection via RJMCMC Open
We propose a fully Bayesian approach to wideband, or broadband, direction-of-arrival (DoA) estimation and signal detection. Unlike previous works in wideband DoA estimation and detection, where the signals were modeled in the time-frequenc…
View article: Personalised antimicrobial susceptibility testing with clinical prediction modelling informs appropriate antibiotic use
Personalised antimicrobial susceptibility testing with clinical prediction modelling informs appropriate antibiotic use Open
Antimicrobial susceptibility testing is a key weapon against antimicrobial resistance. Diagnostic microbiology laboratories use one-size-fits-all testing approaches that are often imprecise, inefficient, and inequitable. Here, we report a …
View article: A qualitative exploration of barriers to efficient and effective structured medication reviews in primary care: Findings from the DynAIRx study
A qualitative exploration of barriers to efficient and effective structured medication reviews in primary care: Findings from the DynAIRx study Open
Introduction Structured medication reviews (SMRs), introduced in the United Kingdom (UK) in 2020, aim to enhance shared decision-making in medication optimisation, particularly for patients with multimorbidity and polypharmacy. Despite its…
View article: Identifying Diagnostic Arguments in Abstract Argumentation1
Identifying Diagnostic Arguments in Abstract Argumentation1 Open
This demo paper introduces an application that is capable of identifying and visualising diagnostic arguments within abstract argumentation systems. The software presented is underpinned by a novel algorithm, called the Diagnostic Argument…
View article: Intrinsically Interpretable Decision Trees for Healthcare Applications
Intrinsically Interpretable Decision Trees for Healthcare Applications Open
The deployment of machine learning for high-stakes decision support may demand algorithms that are intrinsically interpretable so that a model can be interrogated by a user to understand how the model arrived at an output given an input. D…
View article: A qualitative exploration of barriers to efficient and effective Structured Medication Reviews in Primary Care: Findings from the DynAIRx study
A qualitative exploration of barriers to efficient and effective Structured Medication Reviews in Primary Care: Findings from the DynAIRx study Open
Introduction Structured medication reviews (SMRs), introduced in the United Kingdom (UK) in 2020, aim to enhance shared decision-making in medication optimisation, particularly for patients with multimorbidity and polypharmacy. Despite its…
View article: Identifying Drug–Drug Interactions in Spontaneous Reports Utilizing Signal Detection and Biological Plausibility Aspects
Identifying Drug–Drug Interactions in Spontaneous Reports Utilizing Signal Detection and Biological Plausibility Aspects Open
Translational approaches can benefit post‐marketing drug safety surveillance through the growing availability of systems pharmacology data. Here, we propose a novel Bayesian framework for identifying drug–drug interaction (DDI) signals and…
View article: Bayesian Calibration to Address the Challenge of Antimicrobial Resistance: A Review
Bayesian Calibration to Address the Challenge of Antimicrobial Resistance: A Review Open
Antimicrobial resistance (AMR) emerges when disease-causing microorganisms develop the ability to withstand the effects of antimicrobial therapy. This phenomenon is often fueled by the human-to-human transmission of pathogens and the overu…
View article: An $\mathcal{O}(\log_2N)$ SMC$^2$ Algorithm on Distributed Memory with an Approx. Optimal L-Kernel
An $\mathcal{O}(\log_2N)$ SMC$^2$ Algorithm on Distributed Memory with an Approx. Optimal L-Kernel Open
Calibrating statistical models using Bayesian inference often requires both accurate and timely estimates of parameters of interest. Particle Markov Chain Monte Carlo (p-MCMC) and Sequential Monte Carlo Squared (SMC$^2$) are two methods th…
View article: How might dynamic artificial intelligence (DynAIRx) be used to support prescribing to ensure efficient medication reviews?
How might dynamic artificial intelligence (DynAIRx) be used to support prescribing to ensure efficient medication reviews? Open
The DynAIRx project aims to develop artificial intelligence (AI) tools to support medication reviews for patients with multimorbidity (people with ≥ 2 chronic conditions), targeting those at greatest risk of medicine-related harm. This ses…
View article: Identifying drug-drug interactions in spontaneous reports utilizing signal detection and biological plausibility aspects
Identifying drug-drug interactions in spontaneous reports utilizing signal detection and biological plausibility aspects Open
Translational approaches can benefit post-marketing drug safety surveillance through the growing availability of systems pharmacology data. Here, we propose a novel Bayesian framework for identifying drug-drug interaction (DDI) signals and…
View article: Non-Myopic Sensor Control for Target Search and Track Using a Sample-Based GOSPA Implementation
Non-Myopic Sensor Control for Target Search and Track Using a Sample-Based GOSPA Implementation Open
This paper is concerned with sensor management for target search and track using the generalised optimal subpattern assignment (GOSPA) metric. Utilising the GOSPA metric to predict future system performance is computationally challenging, …