Models and likelihood are the backbone of modern statistics and data analysis. Anthony Davison here blends theory and practice to provide an integrated text for advanced undergraduate and graduate students, researchers and practitioners. The coverage is unrivalled, with sections on survival analysis, missing data, Markov chains, Markov random fields, point processes, graphical models, simulation and Markov chain Monte Carlo, estimating functions, asymptotic approximations, local likelihood and spline regressions as well as chapters on more standard topics such as the linear model and Bayesian statistics. Each chapter contains a wide range of exercises. Worked examples, aimed at helping users make the transition from theory to practice, abound. Practicals in the S language for developing computing and data analysis skills are also available, together with a library of data sets. These features will ensure that this becomes the standard text and reference in the subject, for both users of statistics and students. Table of Contents Preface 1. Introduction 2. Variation 3. Uncertainty 4. Likelihood 5. Models 6. Stochastic models 7. Estimation and hypothesis testing 8. Linear regression models 9. Designed experiments 10. Nonlinear regression models 11. Bayesian models 12. Conditional and marginal inference