Markov processes is the class of stochastic processes whose past and future are conditionally independent, given their present state. They constitute important models in many applied fields.

After an introduction to the Monte Carlo method, this book describes discrete time Markov chains, the Poisson process and continuous time Markov chains. It also presents numerous applications including Markov Chain Monte Carlo, Simulated Annealing, Hidden Markov Models, Annotation and Alignment of Genomic sequences, Control and Filtering, Phylogenetic tree reconstruction and Queuing networks. The last chapter is an introduction to stochastic calculus and mathematical finance.

Features include:

• The Monte Carlo method, discrete time Markov chains, the Poisson process and continuous time jump Markov processes.
• An introduction to diffusion processes, mathematical finance and stochastic calculus.
• Applications of Markov processes to various fields, ranging from mathematical biology, to financial engineering and computer science.
• Numerous exercises and problems with solutions to most of them