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  • Search: subject:"QA 75 Electronic computers. Computer science"
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Year of publication
Subject
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QA 75 Electronic computers. Computer science 4 G Geography. Anthropology. Recreation 1 Mathematical statistics 1 Probabilities 1
Online availability
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Free 2 Undetermined 2
Type of publication
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Article 3 Book / Working Paper 1
Type of publication (narrower categories)
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Thesis 1
Language
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Undetermined 4
Author
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Abdul Majid, Mazlina 1 Aspinall, R.J. 1 Buckland, S.T. 1 Choo, L.L. 1 Elston, D.A. 1 Jayasinghe, G. 1 Laffey, D.J. 1 MacMillan, D.C. 1 Shaddick, G. 1 Walker, S.G. 1
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Human behaviour modelling: an investigation using traditional discrete event and combined discrete event and agent-based simulation
Abdul Majid, Mazlina - 2011
This thesis presents a comparison between two simulation methods, namely Discrete Event Simulation (DES) and Agent Based Simulation (ABS). In our literature review we identified a gap in comparing the applicability of these methods to modelling human centric service systems. Hence, we have...
Persistent link: https://www.econbiz.de/10009452274
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Modelling correlated count data with covariates
Shaddick, G.; Choo, L.L.; Walker, S.G. - 2007
We introduce an approach for incorporating dependence between outcomes from a Poisson regression model, with the possibility of incorporating covariate information. In common with other approaches, we use a latent process to induce correlation between outcomes. Previous approaches have modelled...
Persistent link: https://www.econbiz.de/10009455708
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The ultimate bluff: a case study of partygaming.com
Laffey, D.J. - 2007
June 2005 was to bring online gambling out of the shadows and into the spotlight. PartyGaming, a start-up formed in 1997, launched a flotation (Initial Public Offering) on the London Stock Exchange that valued the firm at 4.64 pound billion giving it a larger market capitalisation than British...
Persistent link: https://www.econbiz.de/10009455741
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Adapting regression equations to minimize the mean squared error of predictions made using covariate data from a GIS
Elston, D.A.; Jayasinghe, G.; Buckland, S.T.; … - 1997
Regression equations between a response variable and candidate explanatory variables are often estimated using a training set of data from closely observed locations but are then applied using covariate data held in a GIS to predict the response variable at locations throughout a region. When...
Persistent link: https://www.econbiz.de/10009455700
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