Paul Fearnhead

research

∙ 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...

0 Kes Ward, et al. ∙

research

∙ 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...

0 Gaetano Romano, et al. ∙

research

∙ 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...

0 Rachel Carrington, et al. ∙

research

∙ 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...

0 Jie Li, et al. ∙

research

∙ 10/28/2022

Preferential Subsampling for Stochastic Gradient Langevin Dynamics

Stochastic gradient MCMC (SGMCMC) offers a scalable alternative to tradi...

0 Srshti Putcha, et al. ∙

research

∙ 10/13/2022

Detecting A Single Change-point

This chapter overviews some of the work on detecting and estimating the ...

0 Paul Fearnhead, et al. ∙

research

∙ 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...

0 Lorenzo Rimella, et al. ∙

research

∙ 08/23/2022

cpop: Detecting changes in piecewise-linear signals

Changepoint detection is an important problem with applications across m...

0 Paul Fearnhead, et al. ∙

research

∙ 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...

0 Kes Ward, et al. ∙

research

∙ 06/10/2022

Approximating optimal SMC proposal distributions in individual-based epidemic models

Many epidemic models are naturally defined as individual-based models: w...

0 Lorenzo Rimella, et al. ∙

research

∙ 05/19/2022

Continuously-Tempered PDMP Samplers

New sampling algorithms based on simulating continuous-time stochastic p...

0 Matthew Sutton, et al. ∙

research

∙ 04/06/2022

High-dimensional time series segmentation via factor-adjusted vector autoregressive modelling

Piecewise stationarity is a widely adopted assumption for modelling non-...

0 Haeran Cho, et al. ∙

research

∙ 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...

7 Augustin Chevallier, et al. ∙

research

∙ 12/24/2021

Concave-Convex PDMP-based sampling

Recently non-reversible samplers based on simulating piecewise determini...

0 Matthew Sutton, et al. ∙

research

∙ 11/10/2021

PDMP Monte Carlo methods for piecewise-smooth densities

There has been substantial interest in developing Markov chain Monte Car...

0 Augustin Chevallier, et al. ∙

research

∙ 10/15/2021

Fast Online Changepoint Detection via Functional Pruning CUSUM statistics

Many modern applications of online changepoint detection require the abi...

5 Gaetano Romano, et al. ∙

research

∙ 05/16/2021

Collective anomaly detection in High-dimensional VAR Models

There is increasing interest in detecting collective anomalies: potentia...

0 Hyeyoung Maeng, et al. ∙

research

∙ 10/22/2020

Reversible Jump PDMP Samplers for Variable Selection

A new class of Markov chain Monte Carlo (MCMC) algorithms, based on simu...

5 Augustin Chevallier, et al. ∙

research

∙ 10/19/2020

anomaly : Detection of Anomalous Structure in Time Series Data

One of the contemporary challenges in anomaly detection is the ability t...

0 Alex Fisch, et al. ∙

research

∙ 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...

0 Martin Tveten, et al. ∙

research

∙ 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 ...

0 Gaetano Romano, et al. ∙

research

∙ 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...

0 Vincent Runge, et al. ∙

research

∙ 01/09/2020

Semi-automated simultaneous predictor selection for Regression-SARIMA models

Deciding which predictors to use plays an integral role in deriving stat...

0 Aaron Lowther, et al. ∙

research

∙ 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...

0 Chao Zheng, et al. ∙

research

∙ 10/09/2019

Testing for a Change in Mean After Changepoint Detection

While many methods are available to detect structural changes in a time ...

0 Sean Jewell, et al. ∙

research

∙ 09/04/2019

Subset Multivariate Collective And Point Anomaly Detection

In recent years, there has been a growing interest in identifying anomal...

18 Alexander T M Fisch, et al. ∙

research

∙ 08/19/2019

Evaluation of extremal properties of GARCH(p,q) processes

Generalized autoregressive conditionally heteroskedastic (GARCH) process...

0 Fabrizio Laurini, et al. ∙

research

∙ 07/16/2019

Stochastic gradient Markov chain Monte Carlo

Markov chain Monte Carlo (MCMC) algorithms are generally regarded as the...

4 Christopher Nemeth, et al. ∙

research

∙ 01/29/2019

Stochastic Gradient MCMC for Nonlinear State Space Models

State space models (SSMs) provide a flexible framework for modeling comp...

12 Christopher Aicher, et al. ∙

research

∙ 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 ...

0 Toby Dylan Hocking, et al. ∙

research

∙ 06/19/2018

Large-Scale Stochastic Sampling from the Probability Simplex

Stochastic gradient Markov chain Monte Carlo (SGMCMC) has become a popul...

0 Jack Baker, et al. ∙

research

∙ 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 ...

0 Alexander Tristan Maximilian Fisch, et al. ∙

research

∙ 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...

0 Simon Taylor, et al. ∙

research

∙ 02/21/2018

Fast Nonconvex Deconvolution of Calcium Imaging Data

Calcium imaging data promises to transform the field of neuroscience by ...

0 Sean Jewell, et al. ∙

research

∙ 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...

0 Paul Fearnhead, et al. ∙

research

∙ 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...

0 Jack Baker, et al. ∙

research

∙ 09/13/2017

Particle Filters and Data Assimilation

State-space models can be used to incorporate subject knowledge on the u...

0 Paul Fearnhead, et al. ∙

research

∙ 06/16/2017

Control Variates for Stochastic Gradient MCMC

It is well known that Markov chain Monte Carlo (MCMC) methods scale poor...

0 Jack Baker, et al. ∙

research

∙ 03/09/2017

A log-linear time algorithm for constrained changepoint detection

Changepoint detection is a central problem in time series and genomic da...

0 Toby Dylan Hocking, et al. ∙

research

∙ 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...

0 Robert Maidstone, et al. ∙

research

∙ 09/23/2016

Changepoint Detection in the Presence of Outliers

Many traditional methods for identifying changepoints can struggle in th...

0 Paul Fearnhead, et al. ∙

research

∙ 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...

0 James Edwards, et al. ∙

research

∙ 12/23/2014

Particle Metropolis-adjusted Langevin algorithms

This paper proposes a new sampling scheme based on Langevin dynamics tha...

0 Christopher Nemeth, et al. ∙

research

∙ 12/11/2014

Efficient penalty search for multiple changepoint problems

In the multiple changepoint setting, various search methods have been pr...

0 Kaylea Haynes, et al. ∙

research

∙ 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...

0 Chris Nemeth, et al. ∙

research

∙ 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...

0 Christopher Nemeth, et al. ∙

research

∙ 10/16/2009

Efficient Bayesian analysis of multiple changepoint models with dependence across segments

We consider Bayesian analysis of a class of multiple changepoint models....

0 Paul Fearnhead, et al. ∙

Paul Fearnhead

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