Probablistic models are becoming increasingly important in analyzing the huge amount of data being produced by large-scale DNA-sequencing efforts such as the Human Genome Project. For example, hidden Markov models are used for analyzing biological sequences, linguistic-grammar-based probabilistic models for identifying RNA secondary structure, and probabilistic evolutionary models for inferring…
It's a book on using Matlab to numerically solve problems. Includes numerical integration, differentiation and (partial)differential equation solving.
This book was written for a sequence of courses on the theory and application of numerical approximation techniques.
This reader-friendly book explores where approximation methods come from, why they work, why they sometimes don't work, and when to use which of the many techniques that are available. Each chapter begins with the basic, elementary material and gradually builds up to more advanced topics. Likewise, exercises run from simple hand computations, to challenging derivations and minor proofs, to prog…