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Bayesian methods for interaction and design

Record Type:	Electronic resources : Monograph/item
Title/Author:	Bayesian methods for interaction and designedited by John H. Williamson ... [et al.].
other author:	Williamson, John H.
Published:	Cambridge, United Kingdom ; New York, NY, USA :Cambridge University Press,2022.
Description:	xii, 360 p. :ill., digital ;23 cm.
Notes:	Title from publisher's bibliographic system (viewed on 02 Sep 2022).
Subject:	Human-machine systemsMathematical models.
Online resource:	https://doi.org/10.1017/9781108874830
ISBN:	9781108874830

Bayesian methods for interaction and design
Bayesian methods for interaction and design[electronic resource] /edited by John H. Williamson ... [et al.]. - Cambridge, United Kingdom ; New York, NY, USA :Cambridge University Press,2022. - xii, 360 p. :ill., digital ;23 cm.

Title from publisher's bibliographic system (viewed on 02 Sep 2022).

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.

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.

ISBN: 9781108874830Subjects--Topical Terms:

930074
Human-machine systems
--Mathematical models.

LC Class. No.: TA167 / .B39 2022

Dewey Class. No.: 620.82

Bayesian methods for interaction and design
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245 0 0 $a Bayesian methods for interaction and design $h [electronic resource] / $c edited by John H. Williamson ... [et al.].
260 $a Cambridge, United Kingdom ; New York, NY, USA : $b Cambridge University Press, $c 2022.
300 $a xii, 360 p. : $b ill., digital ; $c 23 cm.
500 $a Title from publisher's bibliographic system (viewed on 02 Sep 2022).
505 2 $a 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.
520 $a 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.
650 0 $a Human-machine systems $x Mathematical models. $3 930074
650 0 $a Human engineering $x Mathematics. $3 930075
650 0 $a User interfaces (Computer systems) $3 189334
650 0 $a Bayesian statistical decision theory. $3 182005
700 1 $a Williamson, John H. $3 930073
856 4 0 $u https://doi.org/10.1017/9781108874830