John Zobitz
Exploring Modeling with Data and Differential Equations Using R
Exploring Modeling with Data and Differential Equations Using R
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Excellent- More about Exploring Modeling with Data and Differential Equations Using R
Exploring Modeling with Data and Differential Equations Using R provides a comprehensive introduction to differential equations with applications to the biological and other natural sciences. It emphasizes data science workflows using the R statistical software program and the tidyverse constellation of packages. Only knowledge of calculus is needed, and the text's integrated framework is a stepping stone for further advanced study in mathematics or as a comprehensive introduction to modeling for quantitative natural scientists.
Format: Hardback
Length: 356 pages
Publication date: 29 November 2022
Publisher: Taylor & Francis Ltd
Exploring Modeling with Data and Differential Equations Using R offers a distinctive introduction to differential equations, with applications in the biological and other natural sciences. In addition, the book delves into the parameterization and simulation of stochastic differential equations, providing valuable tools for model analysis and evaluation. This comprehensive framework spans various mathematical disciplines, data science, statistics, and the natural sciences. Throughout the text, a strong emphasis is placed on data science workflows utilizing the R statistical software program and the tidyverse constellation of packages. No prior knowledge of calculus is required, as the book's integrated framework serves as a stepping stone for advanced study in mathematics or as a comprehensive introduction to modeling for quantitative natural scientists.
The text will guide you through:
Modeling with systems of differential equations and developing analytical, computational, and visual solution techniques.
The R programming language, the tidyverse syntax, and developing data science workflows.
Qualitative techniques to analyze a system of differential equations.
Data assimilation techniques (simple linear regression, likelihood or cost functions, and Markov Chain, Monte Carlo Parameter Estimation) to parameterize models from data.
Simulating and evaluating outputs for stochastic differential equation models.
An associated R package provides a framework for computation and visualization of results. You can find it here: https://cran.r-project.org/web/packages/demodelr/index.html.
Weight: 748g
Dimension: 163 x 239 x 23 (mm)
ISBN-13: 9781032259482
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