國立虎尾科技大學 |

Bayesian methods for interaction and design /

Record Type:	Language materials, printed : Monograph/item
Title/Author:	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.
other author:	Banovic, Nikola,
Description:	1 online resource (xii, 360 pages) :digital, PDF file(s). :
Notes:	Title from publisher's bibliographic system (viewed on 02 Sep 2022).
Subject:	Bayesian statistical decision theory. -
Online resource:	https://doi.org/10.1017/9781108874830
ISBN:	9781108874830 (ebook)

Bayesian methods for interaction and design /
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. - 1 online resource (xii, 360 pages) :digital, PDF file(s).

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: 9781108874830 (ebook)Subjects--Topical Terms:

527671
Bayesian statistical decision theory.

LC Class. No.: TA167 / .B39 2022

Dewey Class. No.: 620.8/2

Bayesian methods for interaction and design /
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245 0 0 $a Bayesian methods for interaction and design / $c 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.
264 1 $a Cambridge, United Kingdom ; New York, NY, USA : $b Cambridge University Press, $c 2022.
300 $a 1 online resource (xii, 360 pages) : $b digital, PDF file(s).
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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 Bayesian statistical decision theory. $3 527671
650 0 $a User interfaces (Computer systems) $3 561162
650 0 $a Human engineering $x Mathematics. $3 1442357
650 0 $a Human-machine systems $x Mathematical models. $3 1218756
700 1 $a Banovic, Nikola, $d 1982- $e editor. $3 1442356
700 1 $a Kristensson, Per Ola, $e editor. $3 1442355
700 1 $a Oulasvirta, Antti, $d 1979- $e editor. $3 1442354
700 1 $a Williamson, John H., $d 1980- $e editor. $3 1442353
776 0 8 $i Print version: $z 9781108834995
856 4 0 $u https://doi.org/10.1017/9781108874830