Bayesian Modelling for Data Analysis and Learning from Data
Matthias Seeger · 2006
These notes provide clarifying remarks and definitions complementing the course Bayesian Modelling for Data Analysis and Learning from Data, to be held at IK 2006. 1 Bayesian Statistics for Machine Learning In this Section we define and motivate basic terms of probability and Bayesian statistics relevant for Machine Learning. The linear model is introduced, the notion of complexity control via Occam’s razor is motivated. 1.1 Machine Learning: Managing and Quantifying Uncertainty Machine Learning (ML) is a hybrid of Statistics and algorithmic Computer Science (CS). In this course, we will be mostly concerned with the statistical side. Statistics is about managing and quantifying uncertainty. Uncertainty may arise due to many different reasons, for example: • Measurement noise: Measurements of physical processes are always subject to inaccuracies. Sometimes, low quality data may be obtained more economically. Data items may be missing