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of 6
pro vyhledávání: '"Paul Wu"'
Publikováno v:
Journal of the Royal Statistical Society Series C: Applied Statistics. 70:770-792
Dynamic queueing networks (DQN) model queueing systems where demand varies strongly with time, such as airport terminals. With rapidly rising global air passenger traffic placing increasing pressure on airport terminals, efficient allocation of resou
Publikováno v:
Journal of Statistical Software, Vol 95, Iss 1, Pp 1-29 (2020)
Journal of Statistical Software; Vol 95 (2020); 1-29
Journal of Statistical Software; Vol 95 (2020); 1-29
Large networks of queueing systems model important real-world systems such as MapReduce clusters, web-servers, hospitals, call centers and airport passenger terminals. To model such systems accurately, we must infer queueing parameters from data. Unf
Publikováno v:
Stats
Volume 4
Issue 3
Pages 45-775
Stats, Vol 4, Iss 45, Pp 762-775 (2021)
Volume 4
Issue 3
Pages 45-775
Stats, Vol 4, Iss 45, Pp 762-775 (2021)
Approximate Bayesian computation is a likelihood-free inference method which relies on comparing model realisations to observed data with informative distance measures. We obtain functional data that are not only subject to noise along their y axis b
Publikováno v:
PLoS ONE
PLoS ONE, Vol 16, Iss 6, p e0251723 (2021)
PLoS ONE, Vol 16, Iss 6, p e0251723 (2021)
Peer-grouping is used in many sectors for organisational learning, policy implementation, and benchmarking. Clustering provides a statistical, data-driven method for constructing meaningful peer groups, but peer groups must be compatible with busines
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::bb7b8a21f043c87f474631fa99b96716
http://arxiv.org/abs/2011.08405
http://arxiv.org/abs/2011.08405
Autor:
Paul Wu, A. Charisse Farr, Daniel Simpson, Prasad Yarlagadda, Fabrizio Ruggeri, Kerrie Mengersen
Publikováno v:
International statistical review 88 (2020): 335–353. doi:10.1111/insr.12350
info:cnr-pdr/source/autori:A.C. Farr, K. Mengersen, F. Ruggeri, D. Simpson, P. Wu, and P. Yarlagadda/titolo:Combining opinions for use in Bayesian networks: A measurement error approach/doi:10.1111%2Finsr.12350/rivista:International statistical review/anno:2020/pagina_da:335/pagina_a:353/intervallo_pagine:335–353/volume:88
info:cnr-pdr/source/autori:A.C. Farr, K. Mengersen, F. Ruggeri, D. Simpson, P. Wu, and P. Yarlagadda/titolo:Combining opinions for use in Bayesian networks: A measurement error approach/doi:10.1111%2Finsr.12350/rivista:International statistical review/anno:2020/pagina_da:335/pagina_a:353/intervallo_pagine:335–353/volume:88
Bayesian networks (BNs) are graphical probabilistic models used for reasoning under uncertainty. These models are becoming increasingly popular in a range of fields including engineering, ecology, computational biology, medical diagnosis and forensic
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::a5a4f16fba3e25cae0e8aa3b9c89e78d
Publikováno v:
Journal of Air Transport Management. 49:35-45
With immense and growing pressure on stakeholders in international airport terminals to process passengers faster than previously, there is a great benefit to understanding which factors affect passenger processing times and in which situations. In a