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Books on the topic 'The hierarchical model'

Author: Grafiati
Published: 30 June 2021
Last updated: 5 May 2022

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1

Chan, Hing Kai, and Xiaojun Wang. Fuzzy Hierarchical Model for Risk Assessment. London: Springer London, 2013. http://dx.doi.org/10.1007/978-1-4471-5043-5.

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2

Chen, Y. F. Translation of a hierarchical model to VHDL. Manchester: UMIST, 1996.

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3

Mark, Wilson. Measuring stages of growth: A psychological model of hierarchical development. Hawthorn, Vic., Australia: Australian Council for Educational Research, 1985.

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4

Glen, J. J. A model for promotion rate control in hierarchical manpower systems. Edinburgh: University of Edinburgh, Management School, 1994.

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5

Chan, Hing Kai. Fuzzy Hierarchical Model for Risk Assessment: Principles, Concepts, and Practical Applications. London: Springer London, 2013.

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6

Zawis, John A. Accessing hierarchical databases via SQL transactions in a multi-model database system. Monterey, Calif: Naval Postgraduate School, 1987.

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7

Maos, J. The hierarchical organization of rural service centres: An operational model for regional development planning. Rehovot, Israel: Settlement Study Centre, 1987.

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8

Osano, Hiroshi. The welfare analysis of the social security system in a hierarchical firm model with bargaining. Hikone, Japan: Faculty of Economics, Shiga University, 1985.

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9

Gupta, Amit. Effect of service climate on service quality: Test of a model using hierarchical linear modeling. Bangalore: Indian Institute of Management, 2002.

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10

Burton, Richard M., and Børge Obel, eds. Design Models for Hierarchical Organizations. Boston, MA: Springer US, 1995. http://dx.doi.org/10.1007/978-1-4615-2285-0.

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11

Escobar, Michael D. Computing Bayesian nonparametic hierarchiacal models. Toronto: University of Toronto, Dept. of Statistics, 1998.

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12

Drakopoulos, S. A. Hierarchical behaviour in economics. Aberdeen: University of Aberdeen, Dept. of Economics, 1991.

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13

Congdon, P. Applied Bayesian hierarchical methods. Boca Raton: Chapman & Hall/CRC, 2010.

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14

Congdon, P. Applied Bayesian hierarchical methods. Boca Raton: Chapman & Hall/CRC, 2010.

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15

Garson, G. David. Hierarchical linear modeling: Guide and applications. Thousand Oaks, Calif: Sage Publications, 2013.

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16

Weber, Shlomo. On hierarchical spatial competition. Toronto, Ont: Department of Economics, York University, 1989.

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17

Garson, G. David. Hierarchical linear modeling: Guide and applications. Thousand Oaks, Calif: Sage Publications, 2013.

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18

W, Raudenbush Stephen, ed. Hierarchical linear models: Applications and data analysis methods. Newbury Park: Sage Publications, 1992.

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19

Gelman, Andrew. Data analysis using regression and multilevel/hierarchical models. Cambridge: Cambridge University Press, 2007.

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20

S, Bryk Anthony, ed. Hierarchical linear models: Applications and data analysis methods. 2nd ed. Thousand Oaks: Sage Publications, 2002.

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21

P, Carlin Bradley, and Gelfand Alan E. 1945-, eds. Hierarchical modeling and analysis for spatial data. Boca Raton: Chapman & Hall, 2004.

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22

Drakopoulos, S. A. A hierarchical approach to trade union behaviour. Aberdeen: University of Aberdeen, Dept. of Economics, 1991.

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23

Schröter, Michael. Compact hierarchical bipolar transistor modeling with hicum. Singapore: World Scientific, 2010.

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24

Biological neural networks: Hierarchical concept of brain function. Boston: Birkhäuser, 1998.

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25

Thériault, Marc-Erick. Bayesian hierarchical models for mapping lung cancer mortality in Ontario. Ottawa: National Library of Canada, 2000.

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26

Baldassarre, Gianluca, and Marco Mirolli, eds. Computational and Robotic Models of the Hierarchical Organization of Behavior. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-39875-9.

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27

Sun, Li. Bayesian estimation procedures for one and two-way hierarchical models. Toronto: [s.n.], 1992.

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28

Bernd, Meinerzhagen, ed. Hierarchical device simulation: The Monte-Carlo perspective. Wien: Springer, 2003.

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29

A hierarchical coordinate system for geoprocessing and cartography. Berlin: Springer, 1999.

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30

Spanjers, Willy. Hierarchically structured economies: Models with bilateral exchange institutions. Dordrecht: Kluwer Academic Pub., 1997.

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31

Nes, Ir R. Design of multimodal transport networks: A hierarchical approach. Delft, Netherlands: DUP Science, 2002.

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32

Hesselager, Ole. Estimation of Variance Components in Hierarchical Regression Models with Nested Classification. Copenhagen: Laboratory of Actuarial Mathematics, University of Copenhagen, 1988.

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33

Vanem, Erik. Bayesian Hierarchical Space-Time Models with Application to Significant Wave Height. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-30253-4.

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34

Hendricks, Wallace E. Hierarchical spline models for conditional quantiles and the demand for electricity. Urbana-Champaign, Ill: Bureau of Economic and Business Research, 1990.

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35

Chauvet, G. Theoretical systems in biology: Hierarchial and functional integration. New York: Pergamon, 1995.

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36

Theoretical systems in biology: Hierarchial and functional integration. Oxford, UK: Pergamon, 1996.

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37

Fotheringham, A. Stewart. Hierarchical destination choices: Discussion with evidence from migration in The Netherlands. Hague: Netherlands Interuniversity Demographic Institute, 1987.

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38

Object-oriented simulation with hierarchical, modular models: Intelligent agents and endomorphic systems. Boston: Academic Press, 1990.

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39

Szabo, B. A. Hierarchic plate and shell models based on p-extension. Hampton, Va: Langley Research Center, 1988.

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40

Fuzzy Hierarchical Model for Risk Assessment. Springer London Ltd, 2013.

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41

Heinl, Hans. A hierarchical, integrated process-, resource-, and object model. 2001.

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42

Kruschke, John K., and Wolf Vanpaemel. Bayesian Estimation in Hierarchical Models. Edited by Jerome R. Busemeyer, Zheng Wang, James T. Townsend, and Ami Eidels. Oxford University Press, 2015. http://dx.doi.org/10.1093/oxfordhb/9780199957996.013.13.

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Abstract:
Bayesian data analysis involves describing data by meaningful mathematical models, and allocating credibility to parameter values that are consistent with the data and with prior knowledge. The Bayesian approach is ideally suited for constructing hierarchical models, which are useful for data structures with multiple levels, such as data from individuals who are members of groups which in turn are in higher-level organizations. Hierarchical models have parameters that meaningfully describe the data at their multiple levels and connect information within and across levels. Bayesian methods are very flexible and straightforward for estimating parameters of complex hierarchical models (and simpler models too). We provide an introduction to the ideas of hierarchical models and to the Bayesian estimation of their parameters, illustrated with two extended examples. One example considers baseball batting averages of individual players grouped by fielding position. A second example uses a hierarchical extension of a cognitive process model to examine individual differences in attention allocation of people who have eating disorders. We conclude by discussing Bayesian model comparison as a case of hierarchical modeling.
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43

Yuan, Soe-Tsyr. Knowledge-based decision model construction for hierarchical diagnosis and repair. 1994.

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44

Yuan, Soe-Tsyr. Knowledge-based decision model construction for hierarchical diagnosis and repair. 1994.

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45

Collet, P., and J. P. Eckmann. A Renormalization Group Analysis of the Hierarchical Model in Statistical Mechanics. Springer, 2014.

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46

Wang, Xiaojun, and Hing Kai Chan. Fuzzy Hierarchical Model for Risk Assessment: Principles, Concepts, and Practical Applications. Springer, 2015.

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47

Tenney, James. Hierarchical Temporal Gestalt Perception in Music. Edited by Larry Polansky, Lauren Pratt, Robert Wannamaker, and Michael Winter. University of Illinois Press, 2017. http://dx.doi.org/10.5406/illinois/9780252038723.003.0009.

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James Tenney talks about hierarchical temporal gestalt perception in music based on a metric space model, which he applied to compositions by Anton Webern, Edgard Varèse, and Claude Debussy. He begins with a discussion of temporal gestalt-units (TGs), using the terms “element,” “clang,” and “sequence” to designate TGs at the first three hierarchical levels of perceptual organization. He then considers a number of questions that might be the most relevant to musical perception, such as: how the perceptual boundaries of a TG are determined; or the extent to which the factors involved in temporal gestalt perception are objective. In an effort to provide some tentative answers to such questions, Tenney proposes a hypothesis of temporal gestalt perception and presents some results of a computer analysis program based on this hypothesis. The program, written by Larry Polansky, represents a simplified model of this aspect of musical perception, and Tenney describes some of the implications, limitations, and possible extensions of this model.
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48

Liang, Percy, Michael Jordan, and Dan Klein. Probabilistic grammars and hierarchical Dirichlet processes. Edited by Anthony O'Hagan and Mike West. Oxford University Press, 2018. http://dx.doi.org/10.1093/oxfordhb/9780198703174.013.27.

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This article focuses on the use of probabilistic context-free grammars (PCFGs) in natural language processing involving a large-scale natural language parsing task. It describes detailed, highly-structured Bayesian modelling in which model dimension and complexity responds naturally to observed data. The framework, termed hierarchical Dirichlet process probabilistic context-free grammar (HDP-PCFG), involves structured hierarchical Dirichlet process modelling and customized model fitting via variational methods to address the problem of syntactic parsing and the underlying problems of grammar induction and grammar refinement. The central object of study is the parse tree, which can be used to describe a substantial amount of the syntactic structure and relational semantics of natural language sentences. The article first provides an overview of the formal probabilistic specification of the HDP-PCFG, algorithms for posterior inference under the HDP-PCFG, and experiments on grammar learning run on the Wall Street Journal portion of the Penn Treebank.
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49

1960-, Sinha P. K., and Indian Institute of Management, Ahmedabad., eds. Choice of a retail store and retail store format: A hierarchical logit model. Ahmedabad: Indian Institute of Management, Ahmedabad, 2008.

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50

Serial pattern learning in adults: A hierarchical model of internal representation and movement control. 1988.

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