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Parallelisation of a Common Changepoint Detection Method
S. O. Tickle, I. A. Eckley, P. Fearnhead +1
In recent years, various means of efficiently detecting changepoints in the univariate setting have been proposed, with one popular approach involving minimising a penalised cost f…
Generalized Functional Pruning Optimal Partitioning (GFPOP) for Constrained Changepoint Detection in Genomic Data
Toby Dylan Hocking, Guillem Rigaill, Paul Fearnhead +1
We describe a new algorithm and R package for peak detection in genomic data sets using constrained changepoint algorithms. These detect changes from background to peak regions by…
Large-Scale Stochastic Sampling from the Probability Simplex
Jack Baker, Paul Fearnhead, Emily B Fox +1
Stochastic gradient Markov chain Monte Carlo (SGMCMC) has become a popular method for scalable Bayesian inference. These methods are based on sampling a discrete-time approximation…
A linear time method for the detection of point and collective anomalies
Alexander T. M. Fisch, Idris A. Eckley, Paul Fearnhead
The challenge of efficiently identifying anomalies in data sequences is an important statistical problem that now arises in many applications. Whilst there has been substantial wor…
Motor Unit Number Estimation via Sequential Monte Carlo
Simon Taylor, Chris Sherlock, Gareth Ridall +1
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. Inferenc…
Fast Nonconvex Deconvolution of Calcium Imaging Data
Sean Jewell, Toby Dylan Hocking, Paul Fearnhead +1
Calcium imaging data promises to transform the field of neuroscience by making it possible to record from large populations of neurons simultaneously. However, determining the exac…