Exploring Modeling with Data and Differential Equations Using R

• Main Author: Zobitz, John M.
• Format: eBook Book
• Language: English
• Published: Boca Raton, Fla CRC Press 2023; CRC Press LLC; Chapman & Hall
• Edition: 1
• Subjects: Biological models; Biological models > Data processing; Biological models > Mathematical models; Differential equations; R (Computer program language); visualization; simulation; Equilibrium Solutions; Unstable; Stochastic Differential Equations; Markov Chain Monte Carlo Methods; Data Science; Follow; Ensemble Average; SDE; Jacobian Matrix; Phase Plane; Likelihood Function; Euler's Method; tidyverse; Solution Curves; Differential Equation; Dataset Yeast; parameterization; Data Frame; Counterclockwise; Scatter Plot; computation; Solution Trajectories; Euler Maruyama Method; Spiral Source; Wo; Nullclines; Markov Chain Monte Carlo Parameter; Brownian Motion; Nonlinear Parameter Estimation
• ISBN: 1032261811; 9781032261812; 9781032259482; 1032259485
• DOI: 10.1201/9781003286974

Exploring Modeling with Data and Differential Equations Using R provides a unique introduction to differential equations with applications to the biological and other natural sciences. Additionally, model parameterization and simulation of stochastic differential equations are explored, providing additional tools for model analysis and evaluation. This unified framework sits "at the intersection" of different mathematical subject areas, data science, statistics, and the natural sciences. The text throughout emphasizes data science workflows using the R statistical software program and the tidyverse constellation of packages. Only knowledge of calculus is needed; 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. The text will introduce you to: 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.