Richard G Everitt,Richard Culliford,Felipe Medina-Aguayo et al.
Richard G Everitt et al.
This paper examines methodology for performing Bayesian inference sequentially on a sequence of posteriors on spaces of different dimensions. For this, we use sequential Monte Carlo samplers, introducing the innovation of using deterministi...
A computationally efficient nonparametric approach for changepoint detection [0.03%]
一种高效计算的非参数化切点检测方法
Kaylea Haynes,Paul Fearnhead,Idris A Eckley
Kaylea Haynes
In this paper we build on an approach proposed by Zou et al. (2014) for nonparametric changepoint detection. This approach defines the best segmentation for a data set as the one which minimises a penalised cost function, with the cost func...
F J Medina-Aguayo,A Lee,G O Roberts
F J Medina-Aguayo
Pseudo-marginal Markov chain Monte Carlo methods for sampling from intractable distributions have gained recent interest and have been theoretically studied in considerable depth. Their main appeal is that they are exact, in the sense that ...
Marina I Knight,Guy P Nason,Matthew A Nunes
Marina I Knight
Reliable estimation of long-range dependence parameters is vital in time series. For example, in environmental and climate science such estimation is often key to understanding climate dynamics, variability and often prediction. The challen...
Ferran Espuny-Pujol,Karyn Morrissey,Paul Williamson
Ferran Espuny-Pujol
Survey calibration methods modify minimally sample weights to satisfy domain-level benchmark constraints (BC), e.g. census totals. This allows exploitation of auxiliary information to improve the representativeness of sample data (addressin...
Michael U Gutmann,Ritabrata Dutta,Samuel Kaski et al.
Michael U Gutmann et al.
Increasingly complex generative models are being used across disciplines as they allow for realistic characterization of data, but a common difficulty with them is the prohibitively large computational cost to evaluate the likelihood functi...
Ben Norwood,Rebecca Killick
Ben Norwood
Time series within fields such as finance and economics are often modelled using long memory processes. Alternative studies on the same data can suggest that series may actually contain a 'changepoint' (a point within the time series where ...
Clement Lee,Andrew Garbett,Darren J Wilkinson
Clement Lee
A statistical model assuming a preferential attachment network, which is generated by adding nodes sequentially according to a few simple rules, usually describes real-life networks better than a model assuming, for example, a Bernoulli ran...
Jingnan Xue,Faming Liang
Jingnan Xue
This paper proposes a simple, practical and efficient MCMC algorithm for Bayesian analysis of big data. The proposed algorithm suggests to divide the big dataset into some smaller subsets and provides a simple method to aggregate the subset...
Belinda Hernández,Adrian E Raftery,Stephen R Pennington et al.
Belinda Hernández et al.
Bayesian Additive Regression Trees (BART) is a statistical sum of trees model. It can be considered a Bayesian version of machine learning tree ensemble methods where the individual trees are the base learners. However for datasets where th...