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Paul Fearnhead
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2020 – today
- 2024
- [j34]Paul Fearnhead, Daniel Grose
:
cpop: Detecting Changes in Piecewise-Linear Signals. J. Stat. Softw. 109(7) (2024) - [j33]Alexander Tristan Maximilian Fisch, Daniel Grose, Idris A. Eckley, Paul Fearnhead, Lawrence Bardwell:
anomaly: Detection of Anomalous Structure in Time Series Data. J. Stat. Softw. 110(1) (2024) - [j32]Kes Ward, Gaetano Romano
A constant-per-iteration likelihood ratio test for online changepoint detection for exponential family models. Stat. Comput. 34(3): 99 (2024) - [j31]Ziyang Yang, Idris A. Eckley, Paul Fearnhead:
A communication-efficient, online changepoint detection method for monitoring distributed sensor networks. Stat. Comput. 34(3): 115 (2024) - [j30]Gaetano Romano
A Log-Linear Nonparametric Online Changepoint Detection Algorithm Based on Functional Pruning. IEEE Trans. Signal Process. 72: 594-606 (2024) - [i11]C. Trojan, Paul Fearnhead, Christopher Nemeth:
Diffusion Generative Modelling for Divide-and-Conquer MCMC. CoRR abs/2406.11664 (2024) - [i10]Paul Fearnhead, Sebastiano Grazzi, Christopher Nemeth, Gareth O. Roberts:
Stochastic Gradient Piecewise Deterministic Monte Carlo Samplers. CoRR abs/2406.19051 (2024) - [i9]Paul Fearnhead, Christopher Nemeth, Chris J. Oates, Chris Sherlock:
Scalable Monte Carlo for Bayesian Learning. CoRR abs/2407.12751 (2024) - 2023
- [j29]Edward Austin
Online non-parametric changepoint detection with application to monitoring operational performance of network devices. Comput. Stat. Data Anal. 177: 107551 (2023) - [j28]Matthew Sutton
Concave-Convex PDMP-based Sampling. J. Comput. Graph. Stat. 32(4): 1425-1435 (2023) - [j27]Gaetano Romano
Fast Online Changepoint Detection via Functional Pruning CUSUM Statistics. J. Mach. Learn. Res. 24: 81:1-81:36 (2023) - [j26]Vincent Runge, Toby Dylan Hocking, Gaetano Romano
gfpop: An R Package for Univariate Graph-Constrained Change-Point Detection. J. Stat. Softw. 106(6) (2023) - [j25]Gaurav Agarwal
Semiparametric detection of changepoints in location, scale, and copula. Stat. Anal. Data Min. 16(5): 456-473 (2023) - [c5]Srshti Putcha, Christopher Nemeth, Paul Fearnhead:
Preferential Subsampling for Stochastic Gradient Langevin Dynamics. AISTATS 2023: 8837-8856 - 2022
- [j24]Alexander Tristan Maximilian Fisch, Idris A. Eckley
Subset Multivariate Collective and Point Anomaly Detection. J. Comput. Graph. Stat. 31(1): 574-585 (2022) - [j23]Toby Dylan Hocking, Guillem Rigaill, Paul Fearnhead, Guillaume Bourque:
Generalized Functional Pruning Optimal Partitioning (GFPOP) for Constrained Changepoint Detection in Genomic Data. J. Stat. Softw. 101(10) (2022) - [j22]Fabrizio Laurini
Limit theory and robust evaluation methods for the extremal properties of GARCH(p, q) processes. Stat. Comput. 32(6): 104 (2022) - [j21]Alexander Tristan Maximilian Fisch, Idris A. Eckley
A linear time method for the detection of collective and point anomalies. Stat. Anal. Data Min. 15(4): 494-508 (2022) - [j20]Alexander Tristan Maximilian Fisch
Innovative and Additive Outlier Robust Kalman Filtering With a Robust Particle Filter. IEEE Trans. Signal Process. 70: 47-56 (2022) - [c4]Augustin Chevallier, Frédéric Cazals, Paul Fearnhead:
Efficient computation of the the volume of a polytope in high-dimensions using Piecewise Deterministic Markov Processes. AISTATS 2022: 10146-10160 - [c3]Matthew Sutton, Robert Salomone, Augustin Chevallier, Paul Fearnhead:
Continuously Tempered PDMP samplers. NeurIPS 2022 - [i8]Augustin Chevallier, Frédéric Cazals, Paul Fearnhead:
Efficient computation of the volume of a polytope in high-dimensions using Piecewise Deterministic Markov Processes. CoRR abs/2202.09129 (2022) - [i7]Srshti Putcha, Christopher Nemeth, Paul Fearnhead:
Preferential Subsampling for Stochastic Gradient Langevin Dynamics. CoRR abs/2210.16189 (2022) - [i6]Jie Li, Paul Fearnhead, Piotr Fryzlewicz, Tengyao Wang:
Automatic Change-Point Detection in Time Series via Deep Learning. CoRR abs/2211.03860 (2022) - 2020
- [j19]Simon A. C. Taylor
Motor unit number estimation via sequential Monte Carlo. Comput. Stat. Data Anal. 144: 106845 (2020) - [j18]Toby Dylan Hocking, Guillem Rigaill, Paul Fearnhead, Guillaume Bourque:
Constrained Dynamic Programming and Supervised Penalty Learning Algorithms for Peak Detection in Genomic Data. J. Mach. Learn. Res. 21: 87:1-87:40 (2020) - [j17]Georg Hahn
BayesProject: Fast computation of a projection direction for multivariate changepoint detection. Stat. Comput. 30(6): 1691-1705 (2020) - [j16]Aaron P. Lowther
Semi-automated simultaneous predictor selection for regression-SARIMA models. Stat. Comput. 30(6): 1759-1778 (2020)
2010 – 2019
- 2019
- [j15]Jack Baker
Control variates for stochastic gradient MCMC. Stat. Comput. 29(3): 599-615 (2019) - [j14]Lawrence Bardwell, Paul Fearnhead
Most Recent Changepoint Detection in Panel Data. Technometrics 61(1): 88-98 (2019) - [i5]Christopher Aicher, Srshti Putcha, Christopher Nemeth
Stochastic Gradient MCMC for Nonlinear State Space Models. CoRR abs/1901.10568 (2019) - 2018
- [c2]Jack Baker, Paul Fearnhead, Emily B. Fox, Christopher Nemeth:
Large-Scale Stochastic Sampling from the Probability Simplex. NeurIPS 2018: 6722-6732 - [i4]Alexander Tristan Maximilian Fisch, Idris Arthur Eckley
A linear time method for the detection of point and collective anomalies. CoRR abs/1806.01947 (2018) - [i3]Jack Baker, Paul Fearnhead
Large-Scale Stochastic Sampling from the Probability Simplex. CoRR abs/1806.07137 (2018) - 2017
- [j13]Robert Maidstone
On optimal multiple changepoint algorithms for large data. Stat. Comput. 27(2): 519-533 (2017) - [j12]Kaylea Haynes, Paul Fearnhead
A computationally efficient nonparametric approach for changepoint detection. Stat. Comput. 27(5): 1293-1305 (2017) - [i2]Jack Baker, Paul Fearnhead
Control Variates for Stochastic Gradient MCMC. CoRR abs/1706.05439 (2017) - 2016
- [j11]Paul Fearnhead
Augmentation schemes for particle MCMC. Stat. Comput. 26(6): 1293-1306 (2016) - [i1]James Edwards, Paul Fearnhead, Kevin D. Glazebrook:
On the Identification and Mitigation of Weaknesses in the Knowledge Gradient Policy for Multi-Armed Bandits. CoRR abs/1607.05970 (2016) - 2014
- [j10]Christopher Nemeth
Sequential Monte Carlo Methods for State and Parameter Estimation in Abruptly Changing Environments. IEEE Trans. Signal Process. 62(5): 1245-1255 (2014) - 2012
- [c1]Christopher Nemeth, Paul Fearnhead, Lyudmila Mihaylova, Dave Vorley:
Bearings-only tracking with particle filtering for joint parameter learning and state estimation. FUSION 2012: 824-831 - 2011
- [j9]Paul Fearnhead
Efficient Bayesian analysis of multiple changepoint models with dependence across segments. Stat. Comput. 21(2): 217-229 (2011)
2000 – 2009
- 2008
- [j8]Paul Fearnhead
Computational methods for complex stochastic systems: a review of some alternatives to MCMC. Stat. Comput. 18(2): 151-171 (2008) - [j7]Paul Fearnhead
Editorial: Special issue on adaptive Monte Carlo methods. Stat. Comput. 18(4): 341-342 (2008) - 2006
- [j6]Paul Fearnhead
SequenceLDhot: detecting recombination hotspots. Bioinform. 22(24): 3061-3066 (2006) - [j5]Paul Fearnhead
Exact and efficient Bayesian inference for multiple changepoint problems. Stat. Comput. 16(2): 203-213 (2006) - 2005
- [j4]Paul Fearnhead
Direct simulation for discrete mixture distributions. Stat. Comput. 15(2): 125-133 (2005) - [j3]Paul Fearnhead
Exact Bayesian curve fitting and signal segmentation. IEEE Trans. Signal Process. 53(6): 2160-2166 (2005) - 2004
- [j2]Paul Fearnhead
Particle filters for mixture models with an unknown number of components. Stat. Comput. 14(1): 11-21 (2004) - [j1]Paul Fearnhead
Filtering recursions for calculating likelihoods for queues based on inter-departure time data. Stat. Comput. 14(3): 261-266 (2004)
Coauthor Index
aka: Idris Arthur Eckley
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last updated on 2024-10-07 21:23 CEST by the dblp team
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