Finite Markov Chains and Algorithmic Applications [Paperback]

In this 2002 book, the author develops the necessary background in probability theory and Markov chains then discusses important computing applications.Based on a lecture course given at Chalmers University, this book is ideal for advanced undergraduate or beginning graduate students. The author first develops the necessary background in probability theory before applying it to study a range of randomised algorithms that have important applications in computing. This book will appeal not only to mathematicians, but to students of computer science who will find much here that appeals. This is a clear and concise introduction to this subject and the numerous exercises included will help students to deepen their understanding.Based on a lecture course given at Chalmers University, this book is ideal for advanced undergraduate or beginning graduate students. The author first develops the necessary background in probability theory before applying it to study a range of randomised algorithms that have important applications in computing. This book will appeal not only to mathematicians, but to students of computer science who will find much here that appeals. This is a clear and concise introduction to this subject and the numerous exercises included will help students to deepen their understanding.This text is ideal for advanced undergraduate or beginning graduate students. The author first develops the necessary background in probability theory and Markov chains before using it to study a range of randomized algorithms with important applications in optimization and other problems in computing. The book will appeal not only to mathematicians, but to students of computer science who will discover much useful material. This clear and concise introduction to the subject has numerous exercises that will help students to deepen their understanding.1. Basics of probability theory; 2. Markov chains; 3. Computer simulation of Markov chains; 4. Irreducible and aperiodic Markov cl#O