Markov Processes and Applications
ebook ∣ Algorithms, Networks, Genome and Finance · Wiley in Probability and Statistics
By Etienne Pardoux

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Jean-François Le Gall, Professor at Université de Paris-Orsay, France.
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.
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