A very short introduction on Hidden Markov Models.
Originally presented as a ShiftForward Tech Talk, a series of weekly talks presented by ShiftForward employees.
The talk starts by describing the Markov property as an introduction to Markov Models. Markov Chains are then presented, serving as the example of fully observable autonomous Markov Models. An example of a Weather Markov Chain illustrates some applications for the model. Markov Chains are then extended to Hidden Markov Models. The elements of an HMM are described, and the three basic problems of HMMs are introduced:
1. Determining the probability of a sequence of observations having been generated by a given model.
2. Determining the state sequence which best explains a sequence of observations.
3. Adjusting the model parameters in order to maximize the probability of generating a given sequence of observations.
Algorithms to solve the three problems are introduced, and some conclusions are drawn on the subject.