Statistical modelling and inference

Abstract

The modern view of statistical machine learning and signal processing is that the central task is one of finding good probabilistic models for the joint distribution over all the variables in the problem. We can then make `queries' of this model, also known as inferences, to determine optimal parameter values or signals. Hence, the importance of statistical methods to this book cannot be overstated. This chapter is an in-depth exploration of what this probabilistic modeling entails, the origins of the concepts involved, how to perform inferences and how to test the quality of a model produced this way.