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1

Hastie, Trevor. Generalized additive models. Boca Raton, Fla: Chapman & Hall/CRC, 1999.

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2

Hastie, Trevor. Generalized additive models. Toronto: University of Toronto, Dept. of Statistics, 1985.

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3

Hastie, Trevor. Generalized additive models. London: Chapman and Hall, 1990.

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4

Hastie, Trevor. Generalized additive models: Some applications. Toronto: University of Toronto, Dept. of Statistics, 1985.

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5

Yee, Thomas W. Vector Generalized Linear and Additive Models. New York, NY: Springer New York, 2015. http://dx.doi.org/10.1007/978-1-4939-2818-7.

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6

Berhane, Kiros. Generalized additive models for longitudinal data. [Toronto, Ont: University of Toronto, Department of Statistics, 1993.

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7

Hastie, Trevor. Generalized additive models, cubic splines and personalized likelihood. Toronto: University of Toronto, Dept. of Statistics, 1987.

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8

Kobayashi, Donald R. Evaluation of time-area closures to reduce incidental sea turtle take in the Hawaii-based longline fishery: Generalized Additive Model (GAM) development and retrospective examination. Honolulu, Hawaii: U.S. Dept. of Commerce, National Oceanic and Atmospheric Administration, National Marine Fisheries Service, Pacific Islands Fisheries Science Center, 2005.

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9

Hastie, T. J. Generalized Additive Models. CRC Press LLC, 2017.

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10

Wood, Simon N. Generalized Additive Models. Chapman and Hall/CRC, 2017. http://dx.doi.org/10.1201/9781315370279.

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11

Wood, Simon N. Generalized Additive Models. Chapman and Hall/CRC, 2006. http://dx.doi.org/10.1201/9781420010404.

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12

Hastie, T. J., and R. J. Tibshirani. Generalized Additive Models. Routledge, 2017. http://dx.doi.org/10.1201/9780203753781.

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13

Generalized Additive Models. CRC Press LLC, 2017.

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14

Hastie, T. J. Generalized Additive Models. CRC Press LLC, 2017.

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15

Hastie, T. J. Generalized Additive Models. CRC Press LLC, 2017.

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16

Wood, Simon N. Generalized Additive Models. Taylor & Francis Group, 2006.

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17

Wood, Simon N. Generalized Additive Models: An Introduction with R. Taylor & Francis Group, 2006.

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18

Generalized Additive Models: An Introduction with R, Second Edition. Taylor & Francis Group, 2017.

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19

Wood, Simon N. Generalized Additive Models: An Introduction with R, Second Edition. Taylor & Francis Group, 2017.

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20

Wood, Simon N. Generalized Additive Models: An Introduction with R, Second Edition. Taylor & Francis Group, 2017.

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21

Wood, Simon N. Generalized Additive Models: An Introduction with R, Second Edition. Taylor & Francis Group, 2017.

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22

Wood, Simon N. Generalized Additive Models: An Introduction with R, Second Edition. Taylor & Francis Group, 2017.

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23

Wood, Simon. Generalized Additive Models: An Introduction with R, Second Edition. Taylor & Francis Group, 2017.

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24

A Beginner's Guide to Generalized Additive Mixed Models with R. New York, USA: Highland Statistics Ltd, 2014.

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25

Generalized Additive Models: An Introduction with R (Texts in Statistical Science). Chapman & Hall/CRC, 2006.

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26

Yee, Thomas W. Vector Generalized Linear and Additive Models: With an Implementation in R. Springer, 2016.

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27

Yee, Thomas W. Vector Generalized Linear and Additive Models: With an Implementation in R. Springer London, Limited, 2015.

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28

Vector Generalized Linear and Additive Models: With an Implementation in R. Springer New York, 2015.

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29

Succi, Sauro. Advanced RLB models. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780199592357.003.0035.

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The relativistic LB scheme described in Chapter 34 is based on the top-down approach and limited to weakly relativistic fluids. This chapter presents a systematic derivation of relativistic LB based on the continuum kinetic theory. The resulting scheme can handle relativistic flows with Lorentz factors up to order ten, thereby considerably extending the scope of the method. In addition, the extension of the LB scheme to generalized coordinates for the simulation of flows on curved manifolds, is also illustrated.
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30

Newman, Mark. Models of network formation. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780198805090.003.0013.

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This chapter describes models of the growth or formation of networks, with a particular focus on preferential attachment models. It starts with a discussion of the classic preferential attachment model for citation networks introduced by Price, including a complete derivation of the degree distribution in the limit of large network size. Subsequent sections introduce the Barabasi-Albert model and various generalized preferential attachment models, including models with addition or removal of extra nodes or edges and models with nonlinear preferential attachment. Also discussed are node copying models and models in which networks are formed by optimization processes, such as delivery networks or airline networks.
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31

Mann, Peter. Lagrangian Field Theory. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780198822370.003.0025.

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In this chapter, Hamiltonian field theory is derived classically via a Hamiltonian density, using the zeroth component of a 4-momentum density. In field theory, space and time are considered to be on equal footing but, in the canonical formalism, time is treated as being special and therefore, by definition, it is not covariant. Consequently, most field theoretic models are built on Lagrangian formulations. A covariant canonical formalism is the subject of the de Donder–Weyl formalism, which is briefly discussed as a covariant Hamiltonian field theory. In addition, the chapter examines the case of a generalised Poisson bracket in the continuous form for two local smooth functionals of phase space.
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32

Bartkowicz, Leszek. Tekstura drzewostanów naturalnych w polskich parkach narodowych na tle teorii dynamiki lasu. Publishing House of the University of Agriculture in Krakow, 2021. http://dx.doi.org/10.15576/978-83-66602-20-5.

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The aim of the study was to compare a patch-mosaic pattern in the old-growth forest stands developed in various climate and soil conditions occurring in different regions of Poland. Based on the assumption, that the patch-mosaic pattern in the forest reflect the dynamic processes taking place in it, and that each type of forest ecosystem is characterized by a specific regime of natural disturbances, the following hypotheses were formulated: (i) the patches with a complex structure in stands composed of latesuccessional, shade-tolerant tree species are more common than those composed of early-successional, light-demanding ones, (ii) the patch-mosaic pattern is more heterogeneous in optimal forest site conditions than in extreme ones, (iii) in similar site conditions differentiation of the stand structure in distinguished patches is determined by the successional status of the tree species forming a given patch, (iv) the successional trends leading to changes of species composition foster diversification of the patch structure, (v) differentiation of the stand structure is negatively related to their local basal area, especially in patches with a high level of its accumulation. Among the best-preserved old-growth forest remaining under strict protection in the Polish national parks, nineteen research plots of around 10 ha each were selected. In each plot, a grid (50 × 50 m) of circular sample subplots (with radius 12,62 m) was established. In the sample subplots, species and diameter at breast height of living trees (dbh ≥ 7 cm) were determined. Subsequently, for each sample subplot, several numerical indices were calculated: local basal area (G), dbh structure differentiation index (STR), climax index (CL) and successional index (MS). Statistical tests of Kruskal- Wallis, Levene and Generalized Additive Models (GAM) were used to verify the hypotheses. All examined forests were characterized by a large diversity of stand structure. A particularly high frequency of highly differentiated patches (STR > 0,6) was recorded in the alder swamp forest. The patch mosaic in the examined plots was different – apart from the stands with a strongly pronounced mosaic character (especially subalpine spruce forests), there were also stands with high spatial homogeneity (mainly fir forests). The stand structure in the distinguished patches was generally poorly related to the other studied features. Consequently, all hypotheses were rejected. These results indicate a very complex, mixed pattern of forest natural dynamics regardless of site conditions. In beech forests and lowland multi-species deciduous forests, small-scale disturbances of the gap dynamics type dominate, which are overlapped with less frequent medium-scale disturbances. In more difficult site conditions, large-scale catastrophic disturbances, which occasionally appear in communities formed under the influence of gap dynamics (mainly spruce forests) or cohort dynamics (mainly pine forests), gain importance.
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33

Zocchi, Giovanni. Molecular Machines. Princeton University Press, 2018. http://dx.doi.org/10.23943/princeton/9780691173863.001.0001.

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This book presents a dynamic new approach to the physics of enzymes and DNA from the perspective of materials science. Unified around the concept of molecular deformability—how proteins and DNA stretch, fold, and change shape—the book describes the complex molecules of life from the innovative perspective of materials properties and dynamics, in contrast to structural or purely chemical approaches. It covers a wealth of topics, including nonlinear deformability of enzymes and DNA; the chemo-dynamic cycle of enzymes; supra-molecular constructions with internal stress; nano-rheology and viscoelasticity; and chemical kinetics, Brownian motion, and barrier crossing. Essential reading for researchers in materials science, engineering, and nanotechnology, the book also describes the landmark experiments that have established the materials properties and energy landscape of large biological molecules. The book gives graduate students a working knowledge of model building in statistical mechanics, making it an essential resource for tomorrow's experimentalists in this cutting-edge field. In addition, mathematical methods are introduced in the bio-molecular context. The result is a generalized approach to mathematical problem solving that enables students to apply their findings more broadly.
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