Exploring Modeling with Data and Differential Equations Using R

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A01=John Zobitz
Author_John Zobitz
Brownian Motion
Category=GPH
Category=PBKJ
Category=PBWH
Counterclockwise
Data Frame
data-driven differential equation analysis
Dataset Yeast
Differential Equation
Ensemble Average
eq_isMigrated=1
eq_nobargain
Equilibrium Solutions
Euler Maruyama Method
Euler's Method
Follow
Jacobian Matrix
Likelihood Function
Markov Chain Monte Carlo Methods
Markov Chain Monte Carlo Parameter
mathematical modeling
nonlinear dynamics
Nonlinear Parameter Estimation
Nullclines
parameter estimation
Phase Plane
quantitative biology
Scatter Plot
SDE
Solution Curves
Solution Trajectories
Spiral Source
statistical inference
Stochastic Differential Equations
stochastic processes
Unstable
Wo

Product details

  • ISBN 9781032259482
  • Weight: 1720g
  • Dimensions: 156 x 234mm
  • Publication Date: 29 Nov 2022
  • Publisher: Taylor & Francis Ltd
  • Publication City/Country: GB
  • Product Form: Hardback
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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. It can be found here: https://cran.r-project.org/web/packages/demodelr/index.html.

John Zobitz is a Professor of Mathematics and Data Science at Augsburg University in Minneapolis, Minnesota. His scholarship in environmental data science includes ecosystem models parameterized with datasets from environmental observation networks. He is a member of the Mathematical Association of America (MAA) and previous president of the North Central Section of the MAA. He has served on the editorial board of MAA Notes. He was a recipient of the Fulbright-Saastamoinen Foundation Grant in Health and Environmental Sciences at the University of Eastern Finland in Kuopio, Finland. In addition, he is an affiliated member of the Ecological Forecasting Network and regularly taught at Fluxcourse, an annual summer course for measurements and modeling of ecosystem biogeochemical fluxes.

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