Hidden Markov Models

This document contains slides from a lecture on Hidden Markov Models given by Andrew W. Moore. The slides introduce Markov systems as having a set of states and discrete time steps, with the system occupying exactly one state at each time step chosen randomly based on the previous state. The slides provide examples of state transition probabilities in a Markov system and note that the Markov property means the next state depends only on the current state.