Designed for classroom use, this book has been extensively tested by the authors, and has homework problems carefully designed to develop students' computational skills. It is based on an unorthodox combination of deterministic and probablistic methodologies that are naturally bridged through examples and painlessly introduces students to advanced themes in a natural progression.
Designed for classroom use, this book has been extensively tested by the authors, and has homework problems carefully designed to develop students' computational skills. It is based on an unorthodox combination of deterministic and probablistic methodologies that are naturally bridged through examples and painlessly introduces students to advanced themes in a natural progression.
Daniela Calvetti is a Professor of Mathematics at Case Western Reserve University. Her research interests include numerical linear algebra, large scale scientific computing, Bayesian statistical computing and modeling. She has published over 100 research papers and one monograph.
Inhaltsangabe
1. Chapter 1: Review of Multivariate Calculus and Differential Equations 2. Chapter 2: Compartment Models 3. Chapter 3: From Compartment Models to Continuous Models 4. Chapter 4: Dimensional Analysis and Scaling 5. Chapter 5: Introduction to Stochastic Modeling 6. Chapter 6: Modeling of Noise 7. Chapter 7: Modeling with Waiting Processes 8. Chapter 8: Markov Processes 9. Chapter 9: Cellular Automata, Agent Based Modeling