Probability and Time

(11th-September-2020)

• model a dynamic system as a belief network by treating a feature at a particular time as a random variable. We first give a model in terms of states and then show how it can be extended to features.

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1. Markov Chains

2. Hidden Markov Models

3. Localization

4. Algorithms for Monitoring and Smoothing

5. Dynamic Belief Networks

6. Time Granularity

Markov Chains

• A Markov chain is a special sort of belief network used to represent sequences of values, such as the sequence of states in a dynamic system or the sequence of words in a sentence.