In this article, the notations behind expressing an Hidden Markov Model are explored through formal definitions, state diagrams and examples.

This article explores the underlying assumption of a Markov Chain through an example. A probabilistic automaton and a mathematical representation of the chain are discussed.

This article discusses the various properties of a Gaussian distribution, reasons why it is a valid probability distribution followed by mathematical derivations of its mean and variance.