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02/09/2023
A Constant-per-Iteration Likelihood Ratio Test for Online Changepoint Detection for Exponential Family Models
Online changepoint detection algorithms that are based on likelihood-rat...
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02/06/2023
A Log-Linear Non-Parametric Online Changepoint Detection Algorithm based on Functional Pruning
Online changepoint detection aims to detect anomalies and changes in rea...
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01/13/2023
Improving Power by Conditioning on Less in Post-selection Inference for Changepoints
Post-selection inference has recently been proposed as a way of quantify...
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11/07/2022
Automatic Change-Point Detection in Time Series via Deep Learning
Detecting change-points in data is challenging because of the range of p...
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10/28/2022
Preferential Subsampling for Stochastic Gradient Langevin Dynamics
Stochastic gradient MCMC (SGMCMC) offers a scalable alternative to tradi...
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10/13/2022
Detecting A Single Change-point
This chapter overviews some of the work on detecting and estimating the ...
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08/24/2022
Inference on Extended-Spectrum Beta-Lactamase Escherichia coli and Klebsiella pneumoniae data through SMC^2
We propose a novel stochastic model for the spread of antimicrobial-resi...
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08/23/2022
cpop: Detecting changes in piecewise-linear signals
Changepoint detection is an important problem with applications across m...
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08/02/2022
Poisson-FOCuS: An efficient online method for detecting count bursts with application to gamma ray burst detection
Gamma-ray bursts are flashes of light from distant exploding stars. Cube...
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06/10/2022
Approximating optimal SMC proposal distributions in individual-based epidemic models
Many epidemic models are naturally defined as individual-based models: w...
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05/19/2022
Continuously-Tempered PDMP Samplers
New sampling algorithms based on simulating continuous-time stochastic p...
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04/06/2022
High-dimensional time series segmentation via factor-adjusted vector autoregressive modelling
Piecewise stationarity is a widely adopted assumption for modelling non-...
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02/18/2022
Efficient computation of the volume of a polytope in high-dimensions using Piecewise Deterministic Markov Processes
Computing the volume of a polytope in high dimensions is computationally...
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12/24/2021
Concave-Convex PDMP-based sampling
Recently non-reversible samplers based on simulating piecewise determini...
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11/10/2021
PDMP Monte Carlo methods for piecewise-smooth densities
There has been substantial interest in developing Markov chain Monte Car...
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10/15/2021
Fast Online Changepoint Detection via Functional Pruning CUSUM statistics
Many modern applications of online changepoint detection require the abi...
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05/16/2021
Collective anomaly detection in High-dimensional VAR Models
There is increasing interest in detecting collective anomalies: potentia...
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10/22/2020
Reversible Jump PDMP Samplers for Variable Selection
A new class of Markov chain Monte Carlo (MCMC) algorithms, based on simu...
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10/19/2020
anomaly : Detection of Anomalous Structure in Time Series Data
One of the contemporary challenges in anomaly detection is the ability t...
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10/14/2020
Scalable changepoint and anomaly detection in cross-correlated data with an application to condition monitoring
Motivated by a condition monitoring application arising from subsea engi...
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05/04/2020
Detecting Abrupt Changes in the Presence of Local Fluctuations and Autocorrelated Noise
Whilst there are a plethora of algorithms for detecting changes in mean ...
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02/10/2020
gfpop: an R Package for Univariate Graph-Constrained Change-point Detection
In a world with data that change rapidly and abruptly, it is important t...
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01/09/2020
Semi-automated simultaneous predictor selection for Regression-SARIMA models
Deciding which predictors to use plays an integral role in deriving stat...
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11/05/2019
Consistency of a range of penalised cost approaches for detecting multiple changepoints
A common approach to detect multiple changepoints is to minimise a measu...
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10/09/2019
Testing for a Change in Mean After Changepoint Detection
While many methods are available to detect structural changes in a time ...
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09/04/2019
Subset Multivariate Collective And Point Anomaly Detection
In recent years, there has been a growing interest in identifying anomal...
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08/19/2019
Evaluation of extremal properties of GARCH(p,q) processes
Generalized autoregressive conditionally heteroskedastic (GARCH) process...
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07/16/2019
Stochastic gradient Markov chain Monte Carlo
Markov chain Monte Carlo (MCMC) algorithms are generally regarded as the...
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01/29/2019
Stochastic Gradient MCMC for Nonlinear State Space Models
State space models (SSMs) provide a flexible framework for modeling comp...
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09/29/2018
Generalized Functional Pruning Optimal Partitioning (GFPOP) for Constrained Changepoint Detection in Genomic Data
We describe a new algorithm and R package for peak detection in genomic ...
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06/19/2018
Large-Scale Stochastic Sampling from the Probability Simplex
Stochastic gradient Markov chain Monte Carlo (SGMCMC) has become a popul...
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06/05/2018
A linear time method for the detection of point and collective anomalies
The challenge of efficiently identifying anomalies in data sequences is ...
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04/11/2018
Motor Unit Number Estimation via Sequential Monte Carlo
A change in the number of motor units that operate a particular muscle i...
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02/21/2018
Fast Nonconvex Deconvolution of Calcium Imaging Data
Calcium imaging data promises to transform the field of neuroscience by ...
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12/17/2017
Continious-time Importance Sampling: Monte Carlo Methods which Avoid Time-discretisation Error
In this paper we develop a continuous-time sequential importance samplin...
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10/02/2017
sgmcmc: An R Package for Stochastic Gradient Markov Chain Monte Carlo
This paper introduces the R package sgmcmc; which can be used for Bayesi...
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09/13/2017
Particle Filters and Data Assimilation
State-space models can be used to incorporate subject knowledge on the u...
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06/16/2017
Control Variates for Stochastic Gradient MCMC
It is well known that Markov chain Monte Carlo (MCMC) methods scale poor...
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03/09/2017
A log-linear time algorithm for constrained changepoint detection
Changepoint detection is a central problem in time series and genomic da...
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01/06/2017
Detecting changes in slope with an L_0 penalty
Whilst there are many approaches to detecting changes in mean for a univ...
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09/23/2016
Changepoint Detection in the Presence of Outliers
Many traditional methods for identifying changepoints can struggle in th...
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07/20/2016
On the Identification and Mitigation of Weaknesses in the Knowledge Gradient Policy for Multi-Armed Bandits
The Knowledge Gradient (KG) policy was originally proposed for online ra...
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12/23/2014
Particle Metropolis-adjusted Langevin algorithms
This paper proposes a new sampling scheme based on Langevin dynamics tha...
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12/11/2014
Efficient penalty search for multiple changepoint problems
In the multiple changepoint setting, various search methods have been pr...
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02/04/2014
Particle Metropolis adjusted Langevin algorithms for state space models
Particle MCMC is a class of algorithms that can be used to analyse state...
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06/04/2013
Particle approximations of the score and observed information matrix for parameter estimation in state space models with linear computational cost
Poyiadjis et al. (2011) show how particle methods can be used to estimat...
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10/16/2009