Zobrazeno 1 - 10
of 17
pro vyhledávání: '"Richard G. Everitt"'
Publikováno v:
Bayesian Analysis, 2022, Vol.17(1), pp.223-260 [Peer Reviewed Journal]
Particle Markov chain Monte Carlo (pMCMC) is now a popular method for performing Bayesian statistical inference on challenging state space models (SSMs) with unknown static parameters. It uses a particle filter (PF) at each iteration of an MCMC algor
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::b2f424b0b4aef2d79670af1a38cdffb1
http://wrap.warwick.ac.uk/144625/7/WRAP-ensemble-MCMC-accelerating-pseudo-MCMC-state-space-Kalman-Everitt-2020.pdf
http://wrap.warwick.ac.uk/144625/7/WRAP-ensemble-MCMC-accelerating-pseudo-MCMC-state-space-Kalman-Everitt-2020.pdf
Autor:
Peer Nowack, William J. Collins, Apostolos Voulgarakis, Matthew Kasoar, Richard G. Everitt, Laura Mansfield
Publikováno v:
npj Climate and Atmospheric Science, Vol 3, Iss 1, Pp 1-9 (2020)
Summarization: Understanding and estimating regional climate change under different anthropogenic emission scenarios is pivotal for informing societal adaptation and mitigation measures. However, the high computational complexity of state-of-the-art
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::a271ea5375e942edf051a128298b816f
https://ueaeprints.uea.ac.uk/id/eprint/77401/
https://ueaeprints.uea.ac.uk/id/eprint/77401/
Autor:
Tanya Golubchik, Elizabeth M Batty, Ruth R Miller, Helen Farr, Bernadette C Young, Hanna Larner-Svensson, Rowena Fung, Heather Godwin, Kyle Knox, Antonina Votintseva, Richard G Everitt, Teresa Street, Madeleine Cule, Camilla L C Ip, Xavier Didelot, Timothy E A Peto, Rosalind M Harding, Daniel J Wilson, Derrick W Crook, Rory Bowden
Publikováno v:
PLoS ONE, Vol 8, Iss 5, p e61319 (2013)
BackgroundStaphylococcus aureus is a major cause of healthcare associated mortality, but like many important bacterial pathogens, it is a common constituent of the normal human body flora. Around a third of healthy adults are carriers. Recent evidenc
Externí odkaz:
https://doaj.org/article/ceaa09b68e7541248844928c570fc942
Bacteria reproduce clonally but most species recombine frequently, so that the ancestral process is best captured using an ancestral recombination graph. This graph model is often too complex to be used in an inferential setup, but it can be approxim
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::6013d28028b0acc009e4e0ce0a2cb1ea
Publikováno v:
Statistics and Computing
This paper introduces methodology for performing Bayesian inference sequentially on a sequence of posteriors on spaces of different dimensions. We show how this may be achieved through the use of sequential Monte Carlo (SMC) samplers (Del Moral et al
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::475d6588d522abfefc4c049ce3c4cd09
https://centaur.reading.ac.uk/89497/1/Everitt2020_Article_SequentialMonteCarloWithTransf.pdf
https://centaur.reading.ac.uk/89497/1/Everitt2020_Article_SequentialMonteCarloWithTransf.pdf
Approximate Bayesian computation (ABC) methods permit approximate inference for intractable likelihoods when it is possible to simulate from the model. However they perform poorly for high dimensional data, and in practice must usually be used in con
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::9a9566bb4e9be12ac05f09e31ac9fd05
https://centaur.reading.ac.uk/71245/8/10.1007%2Fs11222-017-9764-4.pdf
https://centaur.reading.ac.uk/71245/8/10.1007%2Fs11222-017-9764-4.pdf
Autor:
Richard G. Everitt
Despite the development of sophisticated techniques such as sequential Monte Carlo (Del Moral et al. in J R Stat Soc Ser B 68(3):411–436, 2006), importance sampling (IS) remains an important Monte Carlo method for low dimensional target distributio
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::72eb791f2f4e082c2a64726d4d60c61a
https://centaur.reading.ac.uk/70160/8/10.1007%2Fs00180-017-0729-z.pdf
https://centaur.reading.ac.uk/70160/8/10.1007%2Fs00180-017-0729-z.pdf
Autor:
R. H. Glendinning, Richard G. Everitt
Publikováno v:
Pattern Recognition. 42:115-125
We address the problem of automatically identifying and restoring damaged and contaminated images. We suggest a novel approach based on a semi-parametric model. This has two components, a parametric component describing known physical characteristics
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::faeb122c23e014072844acb0e09086cd
https://doi.org/10.1016/j.patcog.2008.06.009
https://doi.org/10.1016/j.patcog.2008.06.009
Models for which the likelihood function can be evaluated only up to a parameter-dependent unknown normalising constant, such as Markov random field models, are used widely in computer science, statistical physics, spatial statistics, and network ana
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::71e159d8e133088d816c9e049fb059c6
http://arxiv.org/abs/1504.00298
http://arxiv.org/abs/1504.00298
Publikováno v:
Information Fusion. 7:434-447
When performing data fusion, one often measures where targets were and then wishes to deduce where targets currently are. There has been recent research on the processing of such out-of-sequence data. This research has culminated in the development o
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::56c7403e6d74e048fa0127561997e7ea
https://doi.org/10.1016/j.inffus.2005.07.001
https://doi.org/10.1016/j.inffus.2005.07.001