Zobrazeno 1 - 9
of 9
pro vyhledávání: '"Peter A. Thwaites"'
Publikováno v:
Journal of Artificial Intelligence Research. 64:55-107
Conditional preference networks (CP-nets) are a graphical representation of a person's (conditional) preferences over a set of discrete variables. In this paper, we introduce a novel method of quantifying preference for any given outcome based on a C
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::b77beda0d5e5a5f58963d64082b85a35
https://doi.org/10.1613/jair.1.11323
https://doi.org/10.1613/jair.1.11323
Autor:
Peter A. Thwaites, Jim Q. Smith
Publikováno v:
International Journal of Approximate Reasoning. 88:624-639
Chain Event Graphs are probabilistic graphical models designed especially for the analysis of discrete statistical problems which do not admit a natural product space structure. We show here how they can be used for decision analysis, and describe an
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::23102a8b5edf0cf084bd89865f1e0f35
https://doi.org/10.1016/j.ijar.2017.03.004
https://doi.org/10.1016/j.ijar.2017.03.004
Autor:
Claire Keeble, Graham R. Law, Roger C Parslow, Stuart Barber, Peter A. Thwaites, Paul D. Baxter
Publikováno v:
American Journal of Epidemiology. 186:1204-1208
Chain event graphs (CEGs) are a graphical representation of a statistical model derived from event trees. They have previously been applied to cohort studies but not to case-control studies. In this paper, we apply the CEG framework to a Yorkshire, U
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::dfab63909a81761b131acd1e5344e89a
https://doi.org/10.1093/aje/kwx171
https://doi.org/10.1093/aje/kwx171
Publikováno v:
Machine Learning, Optimization, and Data Science ISBN: 9783030137083
LOD
LOD
The problem of computing distances and shortest paths between vertices in graphs is one of the fundamental issues in graph theory. It is of great importance in many different applications, for example, transportation, and social network analysis. How
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::17bd8aac692e121f9efce0fa5bee2dd3
https://doi.org/10.1007/978-3-030-13709-0_17
https://doi.org/10.1007/978-3-030-13709-0_17
Publikováno v:
The International Journal of Biostatistics. 13
Case-control studies are used in epidemiology to try to uncover the causes of diseases, but are a retrospective study design known to suffer from non-participation and recall bias, which may explain their decreased popularity in recent years. Traditi
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::a26e4fdb83f3f1f1898b328cf46bcc2b
https://doi.org/10.1515/ijb-2016-0073
https://doi.org/10.1515/ijb-2016-0073
Autor:
Peter A. Thwaites, Jim Q. Smith
If the influence diagram (ID) depicting a Bayesian game is common knowledge to its players then additional assumptions may allow the players to make use of its embodied irrelevance statements. They can then use these to discover a simpler game which
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::38398736840d7fd95b232983ef61dfc7
Publikováno v:
Artificial Intelligence. 174:889-909
As the Chain Event Graph (CEG) has a topology which represents sets of conditional independence statements, it becomes especially useful when problems lie naturally in a discrete asymmetric non-product space domain, or when much context-specific info
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::89731af922ac3b4182eb6073990ec2bc
https://doi.org/10.1016/j.artint.2010.05.004
https://doi.org/10.1016/j.artint.2010.05.004
Publikováno v:
Electron. J. Statist. 9, no. 2 (2015), 2130-2169
In this paper we develop a formal dynamic version of Chain Event Graphs (CEGs), a particularly expressive family of discrete graph- ical models. We demonstrate how this class links to semi-Markov models and provides a convenient generalization of the
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::92c2ba56482f4c468c6c16c3f2720fd5
https://doi.org/10.1214/15-ejs1068
https://doi.org/10.1214/15-ejs1068
Autor:
Peter A. Thwaites
Publikováno v:
Artificial Intelligence. :291-315
We present the Chain Event Graph (CEG) as a complementary graphical model to the Causal Bayesian Network for the representation and analysis of causally manipulated asymmetric problems. Our focus is on causal identifiability - finding conditions for
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::393a959e172bab90254cf3e7ee99deeb