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Cancer is a complex disease process that spans multiple scales in space and time. Driven by cutting-edge mathematical and computational techniques, in silicobiology provides powerful tools to investigate the mechanistic relationships of genes, cells, and tissues. It enables the creation of experimentally testable hypotheses, the integration of data across scales, and the prediction of tumor progression and treatment outcome (in silicooncology). Drawing on an interdisciplinary group of distinguished international experts, Multiscale Cancer Modelingdiscusses the scientific and technical expertis

Intro -- Vorwort -- Inhaltsverzeichnis -- Abkürzungs- und Symbolverzeichnis -- Einleitung -- Teil I Financial‑Modeling‑Standards -- 1 Lernziele, Aufbau und Case Study -- 2 Executive Summary -- 3 Grundlagen des Financial Modeling -- 3.1 Was sind Modelle und was ist Financial Modeling? -- 3.2 Anforderungsprofil des Modells analysieren und Leistungskatalog definieren -- 3.3 Financial Models in Modulen aufbauen -- 4 Status quo des Financial Modeling in Theorie und Praxis -- 4.1 Literatur zum Financial Modeling -- 4.2 Verschiedene Ansätze - dieselben Ziele -- 5 Financial-Modeling-Standards -- 5.1 Top-10-Financial-Modeling-Standards -- 5.2 150 Financial-Modeling-Standards -- 5.2.1 Problemeingrenzung -- 5.2.2 Modellstrukturierung und -planung -- 5.2.3 Modellaufbau -- 5.2.4 Qualitätssicherung -- 6 Umsetzung der Top-10-Financial‑Modeling-Standards anhand eines Beispiels -- 6.1 Definieren Sie den Modellzweck -- 6.2 Teilen Sie das Problem in voneinander unabhängige Teilprobleme (Module) -- 6.3 Skizzieren Sie den Datenfluss und die Modellstruktur -- 6.4 Trennen Sie Inputs von Outputs -- 6.5 Gestalten Sie die Arbeitsblätter einheitlich -- 6.6 Verwenden Sie einheitliche Formatierungen -- 6.7 Vermeiden Sie komplexe Formeln und verwenden Sie nur einen einzigen Formeltyp -- 6.8 Vermeiden Sie Zirkelbezüge -- 6.9 Setzen Sie Kontrollfunktionen ein -- 6.10 Präsentieren Sie die Ergebnisse professionell -- 7 Zusammenfassung -- Teil II Model Review -- 1 Lernziele, Aufbau und Case Study -- 2 Executive Summary -- 3 Grundlagen des Model Review -- 3.1 Begriff des Model Review -- 3.2 Schritte des Model Review -- 4 Fehler in Financial Models -- 4.1 Qualitative Fehler -- 4.2 Quantitative Fehler -- 5 Error Detection - Erkennen und Auffinden von Fehlern -- 5.1 Durchsicht -- 5.2 Tests -- 5.3 Analyse-Tools -- 6 Anwendungsbeispiele von Analyse‑Tools.

Computer models offer a means of interpreting and analyzing the dynamics of real-world systems ranging from population growth to ozone depletion. Dynamic Modeling introduces an approach to modeling that makes it a more practical, intuitive endeavor. The book enables readers to convert their understanding of a phenomenon to a computer model, and then to run the model and let it yield the inevitable dynamic consequences built into the structure of the model. Dynamic Modeling uses STELLA II software to develop simulation models. Part I provides an introduction to modeling dynamic systems. Part II offers general methods for modeling. Parts III through VIII apply these methods to model real-world phenomena from chemistry, genetics, ecology, economics, and engineering. To develop and execute dynamic simulation models, Dynamic Modeling comes with STELLA II run-time software for Windows-based computers, as well as computer files of sample models used in the book. Dynamic Modeling offers a clear, approachable introduction to the modeling process, and will be of interest in any field where real problems can be illuminated by computer simulation

"Since the 1st edition of this monograph was published in 2004, there have been numerous developments in the statistical and computational methods used in multilevel and longitudinal modeling. Mixed-effects modeling has been solidified as a primary means for accurately and efficiently estimating a wide-variety of multilevel and longitudinal models. More complex models that include cross-level interactions, cross-classified random effects, alternative covariances structures, and the like appear much more frequently in the health and social sciences research literature. Sophisticated mixedeffects modeling procedures are now incorporated in most comprehensive statistical software packages (including R, Stata, and SAS), and thus there is less need for specialized multilevel software"--