Applied Stochastic Modelling

Covers both contemporary and classical aspects of statistics, including survival analysis, Kernel density estimation, Markov chain Monte Carlo, hypothesis testing, regression, bootstrap, and generalised linear models. This work provides the option of using powerful computational tools for stochastic modelling.