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Books on the topic 'Bayes Modelling'

Author: Grafiati

Published: 4 June 2021

Last updated: 21 February 2023

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1

2010), European-Japanese Conference on Information Modelling and Knowledge Bases (20th. Information modelling and knowledge bases XXII. Amsterdam: Ios Press, 2011.

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2

European-Japanese Conference on Information Modelling and Knowledge Bases (18th 2008). Information modelling and knowledge bases XX. Amsterdam: IOS Press, 2009.

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3

Bech, Niels. Modelling of surface combustion straw bales. Roskilde: Risø National Laboratory, 1994.

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4

Hannu, Jaakkola, Kiyoki Yasushi, and Tokuda Takahiro, eds. Information modelling and knowledge bases XIX. Amsterdam: IOS Press, 2008.

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5

Tallinn, Estonia) 2005 European-Japanese Conference on Information Modelling and Knowledge Bases (15th. Proceedings of the 15th European-Japanese conference on information modelling and knowledge bases: May 16-19, 2005, Tallinn, Estonia. Tallinn: Tallinn University of Technology, Dept. of Informatics, 2005.

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6

Jaworski, Barbara, Josef Rebenda, Reinhard Hochmuth, Stephanie Thomas, Michèle Artigue, Inés Gómez-Chacón, Sarah Khellaf, et al. Inquiry in University Mathematics Teaching and Learning. Brno: Masaryk University Press, 2021. http://dx.doi.org/10.5817/cz.muni.m210-9983-2021.

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The book presents developmental outcomes from an EU Erasmus+ project involving eight partner universities in seven countries in Europe. Its focus is the development of mathematics teaching and learning at university level to enhance the learning of mathematics by university students. Its theoretical focus is inquiry-based teaching and learning. It bases all activity on a three-layer model of inquiry: (1) Inquiry in mathematics and in the learning of mathematics in lecture, tutorial, seminar or workshop, involving students and teachers; (2) Inquiry in mathematics teaching involving teachers exploring and developing their own practices in teaching mathematics; (3) Inquiry as a research process, analysing data from layers (1) and (2) to advance knowledge inthe field. As required by the Erasmus+ programme, it defines Intellectual Outputs (IOs) that will develop in the project. PLATINUM has six IOs: The Inquiry-based developmental model; Inquiry communities in mathematics learning and teaching; Design of mathematics tasks and teaching units; Inquiry-based professional development activity; Modelling as an inquiry process; Evalutation of inquiry activity with students. The project has developed Inquiry Communities, in each of the partner groups, in which mathematicians and educators work together in supportive collegial ways to promote inquiry processes in mathematics learning and teaching. Through involving students in inquiry activities, PLATINUM aims to encourage students` own in-depth engagement with mathematics, so that they develop conceptual understandings which go beyond memorisation and the use of procedures. Indeed the eight partners together have formed an inquiry community, working together to achieve PLATINUM goals within the specific environments of their own institutions and cultures. Together we learn from what we are able to achieve with respect to both common goals and diverse environments, bringing a richness of experience and learning to this important area of education. Inquiry communities enable participants to address the tensions and issues that emerge in developmental processes and to recognise the critical nature of the developmental process. Through engaging in inquiry-based development, partners are enabled and motivated to design activities for their peers, and for newcomers to university teaching of mathematics, to encourage their participation in new forms of teaching, design of teaching, and activities for students. Such professional development design is an important outcome of PLATINUM. One important area of inquiry-based activity is that of “modelling” in mathematics. Partners have worked together across the project to investigate the nature of modelling activities and their use with students. Overall, the project evaluates its activity in these various parts to gain insights to the sucess of inquiry based teaching, learning and development as well as the issues and tensions that are faced in putting into practice its aims and goals.

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7

International Symposium on Building Economics (4th 1987 Danish Building Research Institute, Copenhagen). Building economics: CIB proceedings of the fourth International Symposium on Building Economics. : Cost modelling techniques, cost/pricedata bases, design methods, optimization and other integrated automatic processes, buildability. Hørsholm: Danish Building Research Institute, 1987.

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8

Lee, Sik-Yum. Structural Equation Modelling: A Bayesian Approach (Wiley Series in Probability and Statistics). Wiley, 2007.

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9

Yu, Angela J. Bayesian Models of Attention. Edited by Anna C. (Kia) Nobre and Sabine Kastner. Oxford University Press, 2014. http://dx.doi.org/10.1093/oxfordhb/9780199675111.013.025.

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Traditionally, attentional selection has been thought of as arising naturally from resource limitations, with a focus on what might be the most apt metaphor, e.g. whether it is a ‘bottleneck’ or ‘spotlight’. However, these simple metaphors cannot account for the specificity, flexibility, and heterogeneity of the way attentional selection manifests itself in different behavioural contexts. A recent body of theoretical work has taken a different approach, focusing on the computational needs of selective processing, relative to environmental constraints and behavioural goals. They typically adopt a normative computational framework, incorporating Bayes-optimal algorithms for information processing and action selection. This chapter reviews some of this recent modelling work, specifically in the context of attention for learning, covert spatial attention, and overt spatial attention.

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10

Endrjukaite, T., A. Dudko, and H. Jaakkola. Information Modelling and Knowledge Bases XXX. IOS Press, Incorporated, 2019.

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11

Kiyoki, Y., T. Tokuda, and J. Henno. Information Modelling and Knowledge Bases XXIII. IOS Press, Incorporated, 2012.

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12

Jaakkola, H., Y. Kiyoki, and T. Welzer. Information Modelling and Knowledge Bases XXI. IOS Press, Incorporated, 2010.

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13

Tropmann-Frick, Marina, Bernhard Thalheim, Hannu Jaakkola, Yasushi Kiyoki, and Naofumi Yoshida, eds. Information Modelling and Knowledge Bases XXXII. IOS Press, 2020. http://dx.doi.org/10.3233/faia333.

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14

Information modelling and knowledge bases VII. Amsterdam: IOS Press, 1996.

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15

Tropmann-Frick, Marina, Hannu Jaakkola, Bernhard Thalheim, Yasushi Kiyoki, and Naofumi Yoshida, eds. Information Modelling and Knowledge Bases XXXIII. IOS Press, 2022. http://dx.doi.org/10.3233/faia343.

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16

Tropmann-Frick, M., B. Thalheim, and H. Jaakkola. Information Modelling and Knowledge Bases XXXII. IOS Press, Incorporated, 2021.

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17

Tropmann-Frick, M., B. Thalheim, and H. Jaakkola. Information Modelling and Knowledge Bases XXXIII. IOS Press, Incorporated, 2022.

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18

Endrjukaite, T., A. Dudko, and H. Jaakkola. Information Modelling and Knowledge Bases XXX. IOS Press, Incorporated, 2019.

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19

Kiyoki, Y., A. Dahanayake, and J. Huiskonen. Information Modelling and Knowledge Bases XXXI. IOS Press, Incorporated, 2020.

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20

Hannu, Jaakkola, Kangassalo Hannu, and Ohsuga Setsuo, eds. Advances in information modelling and knowledge bases. Amsterdam: IOS Press, 1991.

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21

Gensler, Sonja. Heterogenität in der Präferenzanalyse: Ein Vergleich Von Hierarchischen Bayes-Modellen und Finite-Mixture-Modellen. Deutscher Universitäts Verlag, 2013.

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22

Gensler, Sonja. Heterogenität in der Präferenzanalyse: Ein Vergleich von Hierarchischen Bayes-Modellen und Finite-Mixture-Modellen. Sonja Gensler, 2003.

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23

Kangassalo, Hannu, Setsuo Ohsuga, and Hannu Jaakkola. Information Modelling and Knowledge Bases (Frontiers in Artificial Intelligence and Applications). Ios Pr Inc, 1990.

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24

(Editor), Hannu Kangassalo, Hannu Jaakkola (Editor), and Eiji Kawaguchi (Editor), eds. Information Modelling and Knowledge Bases X (Frontiers in Artificial Intelligence and Applications). Ios Pr Inc, 1999.

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25

Information Modelling and Knowledge Bases V, (Frontiers in Artificial Intelligence and Applications). Ios Pr Inc, 1994.

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26

Jaakkola, Hannu. Information Modelling and Knowledge Bases IV, (Frontiers in Artificial Intelligence and Applications). Ios Pr Inc, 1993.

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27

(Editor), Yasushi Kiyoki, Benkt Wangler (Editor), Hannu Jaakkola (Editor), Hannu Kangassalo (Editor), and B. Wangler (Editor), eds. Information Modelling and Knowledge Bases XVI (Frontiers in Artificial Intelligence and Applications). IOS Press, 2006.

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28

(Editor), Yasushi Kiyoki, Eiji Kawaguchi (Editor), Hannu Jaakkola (Editor), and Hannu Kangassalo (Editor), eds. Information Modelling and Knowledge Bases XV (Frontiers in Artificial Intelligence and Applications). Ios Pr Inc, 2004.

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29

(Editor), Eiji Kawaguchi, Hannu Kangassalo (Editor), and Hannu Jaakkola (Editor), eds. Information Modelling and Knowledge Bases XI (Frontiers in Artificial Intelligence and Applications, 61). Ios Pr Inc, 2000.

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30

Kangassalo, Hannu, and Hannu Jaakkola. Information Modelling and Knowledge Bases III, (Frontiers in Artificial Intelligence and Applications, 13). Ios Pr Inc, 1992.

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31

Jaakkola, Hannu. Information Modelling and Knowledge Bases XII (Frontiers in Artificial Intelligence and Applications, 67). Ios Pr Inc, 2001.

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32

Kangassalo, Hannu. Information Modelling and Knowledge Bases Xiii (Frontiers in Artificial Intelligence and Applications, 73). Ios Pr Inc, 2002.

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33

Nilsson, J. F., and H. Jaakola. Information Modelling and Knowledge Bases VIII, (Frontiers in Artificial Intelligence and Applications, 38). Ios Pr Inc, 1997.

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34

Jaakkola, Hannu. Information Modelling and Knowledge Bases XIV (Frontiers in Artificial Intelligence and Applications, 94). Ios Pr Inc, 2003.

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35

(Editor), Hannu Kangassalo, Hannu Jaakkola (Editor), Setsuo Ohsuga (Editor), and Benkt Wangler (Editor), eds. Information Modelling and Knowledge Bases VI, (Frontiers in Artificial Intelligence and Applications, Volume 26). Ios Pr Inc, 1995.

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36

Jaakkola, H. Advances in Information Modelling and Knowledge Bases (Frontiers in Artificial Intelligence and Applications Ser). Ios Pr Inc, 1990.

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37

Jaakkola, H., T. Welzer Druzovec, Y. Kiyoki, T. Tokuda, and N. Yoshida. Information Modelling and Knowledge Bases XXI: Volume 206 Frontiers in Artificial Intelligence and Applications. IOS Press, 2010.

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38

Information Modelling and Knowledge Bases XVII: Volume 136 Frontiers in Artificial Intelligence and Applications. IOS Press, 2006.

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39

(Editor), Marie Duzi, Hannu Jaakkola (Editor), Yasushi Kiyoki (Editor), and Hannu Kangassalo (Editor), eds. Information Modelling and Knowledge Bases XVIII: Volume 154 Frontiers in Artificial Intelligence and Applications. IOS Press, 2007.

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40

(Editor), P. J. Charrel, Hannu Jaakkola (Editor), Hannu Kangassalo (Editor), and Eiji Kawaguchi (Editor), eds. Information Modelling and Knowledge Bases IX (Frontiers in Artificial Intelligence and Applications, Vol. 45) (Frontiers in Artificial Intelligence and Applications). Ios Pr Inc, 1998.

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41

(Editor), Y. Tanaka, Hannu Kangassalo (Editor), Hannu Jaakkola (Editor), and Akira Y. Yamamoto (Editor), eds. Information Modelling and Knowledge Bases VII (Frontiers in Artificial Intelligence and Applications, Vol. 34) (Frontiers in Artificial Intelligence and Applications, Vol 34). Ios Pr Inc, 1996.

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42

Nozière, Pierre. INRA feeding system for ruminants. Edited by Daniel Sauvant and Luc Delaby. Wageningen Academic Publishers, 2018. http://dx.doi.org/10.3920/978-90-8686-872-8.

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The INRA Feeding System for Ruminants has been renewed to better address emerging challenges for animal nutrition: prevision of productive responses, product quality, animal health and emissions to the environment, in a larger extent of breeding contexts. The new system is mainly built from meta-analyses of large data bases, and modelling. The dietary supply model accounts for digestive interactions and flows of individual nutrients, so that feed values depend on the final ration. Animal requirements account for variability in metabolic efficiency. Various productive and non-productive animal responses to diets are quantified. This book presents the whole system for dairy and meat, large and small ruminant production, including specificities for tropical and Mediterranean areas. The first two sections present biological concepts and equations (with their field of application and statistical accuracy) used to predict intake (including at grazing) and nutrient supply (Section 1), animal’s requirements and multiple responses to diets (Section 2). They apply to net energy, metabolisable protein and amino acids, water, minerals and vitamins. Section 3 presents the use of concepts and equations in rationing with two purposes: (1) diet calculation for a given performance objective; and (2) prediction of the multiple responses of animal to diet changes. Section 4 displays the tables of feed values, and their prevision. All the equations and concepts are embedded in the fifth version of INRAtion® software for practical use.

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43

Gammerman, Alex. Causal Models and Intelligent Data Management. Springer, 2012.

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44

Causal Models and Intelligent Data Management. Springer, 1999.

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45

Singpurwalla, Nozer. Reliability And Risk: A Bayesian Perspective. John Wiley & Sons Inc, 2006.

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