(16th-September-2020)
• Probability can be used to make decisions under uncertainty.
• The posterior probability is used to update an agent's beliefs based on evidence.
• A Bayesian belief network can be used to represent independence in a domain.
• Exact inference can be carried out for sparse graphs (with low treewidth).
• Stochastic simulation can be used for approximate inference.
• A hidden Markov model or a dynamic belief network can be used for probabilistic reasoning in time, such as for localization.
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