Inference and Learning from Data Inference

The maximum-likelihood (ML) formulation is one of the most formidable tools for the solution of inference problems in modern statistical analysis. It allows the estimation of unknown parameters in order to fit probability density functions (pdfs) onto data measurements. We introduce the ML approach in this chapter and limit our discussions to properties that will be relevant for the future developments in the text. The presentation is not meant to be exhaustive, but targets key concepts that will be revisited in later chapters. We also avoid anomalous situations and focus on the main features of ML inference that are generally valid under some reasonable regularity conditions.