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Bayesian methods for interaction and design / edited by John H. Williamson, University of Glasgow, Antti Oulasvirta, Aalto University, Per Ola Kristensson, University of Cambridge, Nikola Banovic, University of Michigan-Ann Arbor.

Cambridge eBooks: Frontlist 2022 Available online

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Format:
Book
Contributor:
Williamson, John H., 1980- editor.
Oulasvirta, Antti, 1979- editor.
Kristensson, Per Ola, editor.
Banovic, Nikola, 1982- editor.
Language:
English
Subjects (All):
Human-machine systems--Mathematical models.
Human-machine systems.
Human engineering--Mathematics.
Human engineering.
User interfaces (Computer systems).
Bayesian statistical decision theory.
Physical Description:
1 online resource (xii, 360 pages) : digital, PDF file(s).
Edition:
1st ed.
Place of Publication:
Cambridge, United Kingdom ; New York, NY, USA : Cambridge University Press, 2022.
Summary:
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.
Contents:
Bayseian statistics / A. Dix
Bayseian information gain to design interaction / W. Liu, O. Rioul, M Beaudouin-Lafon
Bayseian command selection / S. Zhu, X. Fan, F. Tian, X. Bi.
Notes:
Title from publisher's bibliographic system (viewed on 02 Sep 2022).
Includes bibliographical references.
ISBN:
1-108-89066-0
1-108-87483-5
OCLC:
1322837150

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