Features:*Complete introduction to mathematical probability, random variables, and distribution theory.*Concise but broad account of statistical modelling, covering topics such as generalised linear models, survival analysis, time series, and random processes.*Extensive discussion of the key concepts in classical statistics (point estimation, interval estimation, hypothesis testing) and the main techniques in likelihood-based inference.*Detailed introduction to Bayesian statistics and associated topics.*Practical illustration of some of the main computational methods used in modern statistical inference (simulation, boostrap, MCMC).