An introduction to markov process

By: Daniel W. StroockMaterial type: TextTextSeries: Graduate Texts in Mathematics ; Vol. 230Publication details: Heidelberg: Springer-Verlag, [c2005]Description: 203 pISBN: 9783642405228LOC classification: QA274.7Online resources: Click here to access online
Contents:
1. Random Walks, a Good Place to Begin 2. Doeblin’s Theory for Markov Chains 3. Stationary Probabilities 4. More About the Ergodic Properties of Markov Chains 5. Markov Processes in Continuous Time 6. Reversible Markov Processes 7. A Minimal Introduction to Measure Theory
Summary: This book provides a rigorous but elementary introduction to the theory of Markov Processes on a countable state space. It should be accessible to students with a solid undergraduate background in mathematics, including students from engineering, economics, physics, and biology.The corrected and enlarged 2nd edition contains a new chapter in which the author develops computational methods for Markov chains on a finite state space. Most intriguing is the section with a new technique for computing stationary measures, which is applied to derivations of Wilson's algorithm and Kirchoff's formula for spanning trees in a connected graph. --- summary provided by publisher
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Item type Current library Collection Shelving location Call number Status Notes Date due Barcode Item holds
Book Book ICTS
Mathematic Rack No 5 QA274.7 (Browse shelf (Opens below)) Available Billno:95076; Billdate: 2016-08-04 00279
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1. Random Walks, a Good Place to Begin
2. Doeblin’s Theory for Markov Chains
3. Stationary Probabilities
4. More About the Ergodic Properties of Markov Chains
5. Markov Processes in Continuous Time
6. Reversible Markov Processes
7. A Minimal Introduction to Measure Theory

This book provides a rigorous but elementary introduction to the theory of Markov Processes on a countable state space. It should be accessible to students with a solid undergraduate background in mathematics, including students from engineering, economics, physics, and biology.The corrected and enlarged 2nd edition contains a new chapter in which the author develops computational methods for Markov chains on a finite state space. Most intriguing is the section with a new technique for computing stationary measures, which is applied to derivations of Wilson's algorithm and Kirchoff's formula for spanning trees in a connected graph. --- summary provided by publisher

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