Mathematical Modeling in Biology: A Research Methods Approach

Calculus for Biology is a textbook suitable for upper division mathematics and sciences students and graduate-level biology students with a solid background in calculus. It includes sample MATLAB codes and instruction in Appendices.

Format: Paperback / softback
Length: 316 pages
Publication date: 30 January 2023
Publisher: Taylor & Francis Ltd

Features

This textbook is designed for upper division mathematics and sciences students, as well as graduate-level biology students, with minimal pre-requisites beyond a solid background in calculus, such as a calculus I course. It provides a comprehensive introduction to probability theory, covering topics such as random variables, probability distributions, conditional probability, and Bayesian inference.

The book is organized into four chapters, each focusing on a different aspect of probability theory. The first chapter introduces random variables and their properties, including their mean, variance, and standard deviation. The second chapter explores probability distributions, including binomial, Poisson, and normal distributions. The third chapter discusses conditional probability and its applications in insurance and finance. The fourth chapter introduces Bayesian inference, a powerful method for making predictions based on data.

In addition to the theoretical explanations, the textbook includes numerous sample MATLAB codes and instruction in Appendices. These codes allow students to apply the concepts learned in the book to real-world problems and gain hands-on experience with probability theory.

Overall, this textbook is an excellent resource for students seeking to gain a deeper understanding of probability theory and its applications in mathematics, sciences, and biology. Its clear and concise writing style, coupled with the extensive use of examples and MATLAB codes, makes it accessible to students of all backgrounds.

This textbook is designed for upper division mathematics and sciences students, as well as graduate-level biology students, with minimal pre-requisites beyond a solid background in calculus, such as a calculus I course. It provides a comprehensive introduction to probability theory, covering topics such as random variables, probability distributions, conditional probability, and Bayesian inference.

The book is organized into four chapters, each focusing on a different aspect of probability theory. The first chapter introduces random variables and their properties, including their mean, variance, and standard deviation. The second chapter explores probability distributions, including binomial, Poisson, and normal distributions. The third chapter discusses conditional probability and its applications in insurance and finance. The fourth chapter introduces Bayesian inference, a powerful method for making predictions based on data.

In addition to the theoretical explanations, the textbook includes numerous sample MATLAB codes and instruction in Appendices. These codes allow students to apply the concepts learned in the book to real-world problems and gain hands-on experience with probability theory.

Overall, this textbook is an excellent resource for students seeking to gain a deeper understanding of probability theory and its applications in mathematics, sciences, and biology. Its clear and concise writing style, coupled with the extensive use of examples and MATLAB codes, makes it accessible to students of all backgrounds.

Weight: 625g
Dimension: 234 x 156 (mm)
ISBN-13: 9781032206943