Markov Processes and Applications (eBook)

Etienne Pardoux, Centre for Mathematics and Informatics, University of Provence, Marseille, France Professor Pardoux has authored more than 100 research papers and three books, including the French version of this title. A vastly experienced teacher, he has successfully taught all the material in the book to students in Mathematics, Engineering and Biology.

Preface.

1. Simulations and the Monte Carlo method.

1.1 Description of the method.

1.2 Convergence theorems.

1.3 Simulation of random variables.

1.4 Variance reduction techniques.

1.5 Exercises.

2. Markov chains.

2.1 Definitions and elementary properties.

2.2 Examples.

2.3 Strong Markov property.

2.4 Recurrent and transient states.

2.5 The irreducible and recurrent case.

2.6 The aperiodic case.

2.7 Reversible Markov chain.

2.8 Rate of convergence to equilibrium.

2.9 Statistics of Markov chains.

2.10 Exercises.

3. Stochastic algorithms.

3.1 Markov chain Monte Carlo.

3.2 Simulation of the invariant probability.

3.3 Rate of convergence towards the invariant probability.

3.4 Simulated annealing.

3.5 Exercises.

4. Markov chains and the genome.

4.1 Reading DNA.

4.2 The i.i.d. model.

4.3 The Markov model.

4.4 Hidden Markov models.

4.5 Hidden semi-Markov model.

4.6 Alignment of two sequences.

4.7 A multiple alignment algorithm.

4.8 Exercises.

5. Control and filtering of Markov chains.

5.1 Deterministic optimal control.

5.2 Control of Markov chains.

5.3 Linear quadratic optimal control.

5.4 Filtering of Markov chains.

5.5 The Kalman-Bucy filter.

5.6 Linear-quadratic control with partial observation.

5.7 Exercises.

6. The Poisson process.

6.1 Point processes and counting processes.

6.2 The Poisson process.

6.3 The Markov property.

6.4 Large time behaviour.

6.5 Exercises.

7. Jump Markov processes.

7.1 General facts.

7.2 Infinitesimal generator.

7.3 The strong Markov property.

7.4 Embedded Markov chain.

7.5 Recurrent and transient states.

7.6 The irreducible recurrent case.

7.7 Reversibility.

7.8 Markov models of evolution and phylogeny.

7.9 Application to discretized partial differential
equations.

7.10 Simulated annealing.

7.11 Exercises.

8. Queues and networks.

8.1 M/M/1 queue.

8.2 M/M/1/K queue.

8.3 M/M/s queue.

8.4 M/M/s/s queue.

8.5 Repair shop.

8.6 Queues in series.

8.7 M/G/ infinity queue.

8.8 M/G/1 queue.

8.9 Open Jackson network.

8.10 Closed Jackson network.

8.11 Telephone network.

8.12 Kelly networks.

8.13 Exercises.

9. Introduction to mathematical finance.

9.1 Fundamental concepts.

9.2 European options in the discrete model.

9.3 The Black-Scholes model and formula.

9.4 American options in the discrete model.

9.5 American options in the Black-Scholes model.

9.6 Interest rate and bonds.

9.7 Exercises.

10. Solutions to selected exercises.

10.1 Chapter 1.

10.2 Chapter 2.

10.3 Chapter 3.

10.4 Chapter 4.

10.5 Chapter 5.

10.6 Chapter 6.

10.7 Chapter 7.

10.8 Chapter 8.

10.9 Chapter 9.

References

Index.

"Well-written, this book is suitable as a textbook for
teaching a postgraduate course on applied Markov processes."
(Mathmatical Assoc of America, June 2009)

"It does provide a good introduction to each of the five
application areas." (Mathematical Reviews, July 2010)