Intended for researchers and practitioners in interaction design, this book shows how Bayesian models can be brought to bear on problems of interface design and user modelling. It introduces and motivates Bayesian modelling and illustrates how powerful these ideas can be in thinking about human-computer interaction, especially in representing and manipulating uncertainty. Bayesian methods are increasingly practical as computational tools to implement them become more widely available, and offer a principled foundation to reason about interaction design. The book opens with a self-contained tutorial on Bayesian concepts and their practical implementation, tailored for the background and needs of interaction designers. The contributed chapters cover the use of Bayesian probabilistic modelling in a diverse set of applications, including improving pointing-based interfaces; efficient text entry using modern language models; advanced interface design using cutting-edge techniques in Bayesian optimisation; and Bayesian approaches to modelling the cognitive processes of users.
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John H. Williamson is Senior Lecturer in Computing Science at the University of Glasgow. He holds B.Sc. and Ph.D. degrees from the University of Glasgow. His Ph.D. thesis was on continuous models of interactive systems with uncertainty. He is an expert in modelling and machine learning for continuous-control interfaces, multimodal feedback and uncertainty in interactive systems. His work is widely recognised, with several awards at the ACM SIGCHI conference. He is among the leaders of the Computational Interaction movement, and founded the international Summer School on Computational Interaction.
Antti Oulasvirta is Professor of Electrical Engineering and leads the User Interfaces research group at Aalto University and the Interactive AI research program at the Finnish Center for AI. He was previously Senior Researcher at the Max Planck Institute for Informatics. His work has been awarded the Best Paper Award and Best Paper Honorable Mention at CHI 13 times. He has given keynotes at NordiCHI'14, CoDIT'14, EICS'16, IHCI'17, ICWE'19 and Chinese CHI '19. He is a member of the European Laboratory for Learning and Intelligent Systems and of the Finnish Academy of Science and Letters.
Per Ola Kristensson is Professor of Interactive Systems Engineering in the Department of Engineering at the University of Cambridge and a Fellow of Trinity College, Cambridge. In 2007 he co-founded ShapeWriter to commercialise gesture keyboard technology based on his Ph.D. thesis. ShapeWriter won a Google Android ADC50 developer award and was selected as the 8th best iPhone application by Time in 2008. Kristensson has been named an Innovator Under 35 by MIT Technology Review and awarded the ACM UIST Lasting Impact Award, the Royal Society of Edinburgh Early Career Prize, and the Sir Thomas Makdougall Brisbane Medal. He is an Associate Editor of ACM TOCHI and ACM TIIS.
Nikola Banovic is Assistant Professor of Electrical Engineering and Computer Science at the University of Michigan–Ann Arbor. He received his Ph.D. from the Human-Computer Interaction Institute (HCII) at Carnegie Mellon University, and his B.Sc. and M.Sc. degrees from the University of Toronto. His research interests include computational approaches to studying human-computer interaction, with a focus on explainability and interpretability of complex computational systems, and ethical and equitable computing technologies. His publications include award-winning research on methods to study and model human behaviour in premier HCI conferences.
Les informations fournies dans la section « A propos du livre » peuvent faire référence à une autre édition de ce titre.
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Hardcover. Etat : new. Hardcover. Intended for researchers and practitioners in interaction design, this book shows how Bayesian models can be brought to bear on problems of interface design and user modelling. It introduces and motivates Bayesian modelling and illustrates how powerful these ideas can be in thinking about human-computer interaction, especially in representing and manipulating uncertainty. Bayesian methods are increasingly practical as computational tools to implement them become more widely available, and offer a principled foundation to reason about interaction design. The book opens with a self-contained tutorial on Bayesian concepts and their practical implementation, tailored for the background and needs of interaction designers. The contributed chapters cover the use of Bayesian probabilistic modelling in a diverse set of applications, including improving pointing-based interfaces; efficient text entry using modern language models; advanced interface design using cutting-edge techniques in Bayesian optimisation; and Bayesian approaches to modelling the cognitive processes of users. Bayesian modelling is powerful and an increasingly practical tool for thinking about HCI. Intended for researchers and practitioners, this book introduces Bayesian modelling and shows how it can be brought to bear in interaction design applications ranging from improving pointing-based interfaces to modelling the cognitive processes of users. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability. N° de réf. du vendeur 9781108834995
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