Grid-Based Probability Density Methods

Abstract

The generality and computability of the methods described in the Chapter 3 depend largely on the representation of probability distributions functions. A moment representation is attractive since the representation and manipulation of uncertainly is relatively simple, however, as we discussed, it has a number of limitations. Hence, to go further with a Bayesian philosophy we mush find a class of probability distributions that is flexible enough to describe both prior and posterior distributions that is flexible enough to describe both prior and posterior distributions after updating with a nonlinear, coupled sensor description, that can be easily transformed and integrated to accommodate a variety of task descriptions, and that is still computationally tractable.