Stochastic Tools in Mathematics and Science

This introduction to probability-based modeling covers basic shastic tools used in physics, chemistry, engineering and the life sciences. Topics covered include conditional expectations, shastic processes, Langevin equations, and Markov chain Monte Carlo algorithms. The applications include data assimilation, prediction from partial data, spectral analysis and turbulence. A special feature is the systematic analysis of memory effects.