The concept of modelling using graph theory has its origin in several scientific areas, notably statistics, physics, genetics, and engineering. The use of graphical models in applied statistics has increased considerably over recent years and the theory has been greatly developed and extended. This book provides a self-contained introduction to the learning of graphical models from data, and includes detailed coverage of possibilistic networks - a relatively new reasoning tool that allows the user to infer results from problems with imprecise data. One major advantage of graphical modelling is that specialized techniques that have been developed in one field can be transferred into others easily. The methods described here are applied in a number of industries, including a recent quality testing programme at a major car manufacturer.