Motor Unit Number Estimation via Sequential Monte Carlo
Simon Taylor, Chris Sherlock, Gareth Ridall, Paul Fearnhead

TL;DR
This paper introduces a Bayesian sequential Monte Carlo method for estimating the number of motor units in muscles, aiding neuromuscular disease diagnosis and therapy assessment.
Contribution
It presents a novel SMC-based Bayesian inference approach that efficiently estimates motor unit numbers from muscle testing data, leveraging model structure and conjugacy.
Findings
Method accurately estimates motor units in simulations.
Inferences are consistent across different datasets.
Approach improves computational efficiency and accuracy.
Abstract
A change in the number of motor units that operate a particular muscle is an important indicator for the progress of a neuromuscular disease and the efficacy of a therapy. Inference for realistic statistical models of the typical data produced when testing muscle function is difficult, and estimating the number of motor units from these data is an ongoing statistical challenge. We consider a set of models for the data, each with a different number of working motor units, and present a novel method for Bayesian inference, based on sequential Monte Carlo, which provides estimates of the marginal likelihood and, hence, a posterior probability for each model. To implement this approach in practice we require sequential Monte Carlo methods that have excellent computational and Monte Carlo properties. We achieve this by leveraging the conditional independence structure in the model, where…
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