Another extension of Formulation 9.5 is to allow multiple observations, , , , , before making a decision. Each is assumed to belong to an observation space, . A strategy, , now depends on all observations:

(9.29) |

Under the nondeterministic model, is specified for each and . The set is replaced by

(9.30) |

in (9.24) to obtain the optimal action, .

Under the probabilistic model, is specified instead. It is often assumed that the observations are conditionally independent given . This means for any , , and such that , . The condition in (9.26) is replaced by . Applying Bayes' rule, and using the conditional independence of the 's given , yields

(9.31) |

The denominator can be treated as a constant factor that does not affect the optimization. Therefore, it does not need to be explicitly computed unless the optimal expected cost is needed in addition to the optimal action.

Conditional independence allows a dramatic simplification that avoids
the full specification of
. Sometimes the conditional
independence assumption is used when it is incorrect, just to exploit
this simplification. Therefore, a method that uses conditional
independence of observations is often called *naive Bayes*.

Steven M LaValle 2012-04-20