ABSTRACT
This chapter provides the background mathematical notations and concepts needed to understand the rest of the book. The topics discussed in this chapter include basic probability theory and statistics, fundamentals of linear algebra covering the notions of eigenvalues and eigenvectors, propositional and first-order mathematical logics for representing rules, graphs and trees for representing Bayesian networks and influence diagrams, notions of performance measurements for use in classifier algorithms, and a concise introduction to the theory of algorithmic complexity to analyze expected runtime performance of evidence propagation algorithms. Appendix A details our conventions for symbol usage.
