Zobrazeno 1 - 10
of 98
pro vyhledávání: '"David J. Nott"'
Publikováno v:
International Journal of Forecasting. 37:1355-1375
Estimation and prediction in high dimensional multivariate factor stochastic volatility models is an important and active research area, because such models allow a parsimonious representation of multivariate stochastic volatility. Bayesian inference
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::0c2b50192cc42f91a1ebd6f07d1d0ccc
https://doi.org/10.1016/j.ijforecast.2021.05.001
https://doi.org/10.1016/j.ijforecast.2021.05.001
Publikováno v:
Metrika
Multinomial models can be difficult to use when constraints are placed on the probabilities. An exact model checking procedure for such models is developed based on a uniform prior on the full multinomial model. For inference, a nonuniform prior can
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::5296b8669af5daa4e8b82f88906705b8
https://doi.org/10.1007/s00184-021-00811-8
https://doi.org/10.1007/s00184-021-00811-8
Publikováno v:
SIAM/ASA Journal on Uncertainty Quantification. 9:1034-1063
The aim of the history matching method is to locate nonimplausible regions of the parameter space of complex deterministic or stochastic models by matching model outputs with data. It does this via...
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::9621810e165510e79b4625846fd978bd
https://doi.org/10.1137/19m1286694
https://doi.org/10.1137/19m1286694
Publikováno v:
Statistics and Computing. 30:1057-1073
Likelihood-free methods such as approximate Bayesian computation (ABC) have extended the reach of statistical inference to problems with computationally intractable likelihoods. Such approaches perform well for small-to-moderate dimensional problems,
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::1d08116e78f6372ee9eb872c3b491b0e
https://doi.org/10.1007/s11222-020-09933-x
https://doi.org/10.1007/s11222-020-09933-x
Publikováno v:
Statistics and Its Interface. 13:237-249
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::ba6022a25037e010b184f1b86a089fb6
https://doi.org/10.4310/sii.2020.v13.n2.a8
https://doi.org/10.4310/sii.2020.v13.n2.a8
Publikováno v:
Bayesian Analysis.
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::d545b69516531d5cb1a054a0afd46845
https://doi.org/10.1214/22-ba1332
https://doi.org/10.1214/22-ba1332
Autor:
Richard J Wilson, David J Nott
Publikováno v:
Image Analysis and Stereology, Vol 20, Iss 2, Pp 71-78 (2011)
Many images consist of two or more "phases", where a phase is a collection of homogeneous zones. For example, the phases may represent the presence of different sulphides in an ore sample. Frequently, these phases exhibit very little structure, thoug
Externí odkaz:
https://doaj.org/article/a24c965dac224eea94cbe5496d4450ea
Publikováno v:
Statistics and Computing. 31
Bayesian likelihood-free methods implement Bayesian inference using simulation of data from the model to substitute for intractable likelihood evaluations. Most likelihood-free inference methods replace the full data set with a summary statistic befo
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::cd6faa8de1c7afaf161d4d6f636e370f
https://doi.org/10.1007/s11222-021-10053-3
https://doi.org/10.1007/s11222-021-10053-3
Publikováno v:
Statistics and Computing. 30:543-557
Bayesian synthetic likelihood (BSL) is now a well-established method for performing approximate Bayesian parameter estimation for simulation-based models that do not possess a tractable likelihood function. BSL approximates an intractable likelihood
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::f350a6c836b94941d73ec058c957868c
https://doi.org/10.1007/s11222-019-09904-x
https://doi.org/10.1007/s11222-019-09904-x
Publikováno v:
Journal of Computational and Graphical Statistics. 29:97-113
Deep feedforward neural networks (DFNNs) are a powerful tool for functional approximation. We describe flexible versions of generalized linear and generalized linear mixed models incorporating basis functions formed by a DFNN. The consideration of ne
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::b71afa30436bb26eb9d3eaa22c53d899
https://doi.org/10.1080/10618600.2019.1637747
https://doi.org/10.1080/10618600.2019.1637747