David R. Wolf

PO Box 8308, Austin, TX 78713-8308, USA. E-mail: [email protected]

Received: 20 September 1999 / Accepted: 20 October 1999 / Published: 30 October 1999

Abstract: The topic of this paper is a novel Bayesian continuous-basis field representation and inference framework. Within this paper several problems are solved: The maximally informative inference of continuous-basis fields, that is where the basis for the field is itself a continuous object and not representable in a finite manner; the tradeoff between accuracy of representation in terms of information learned, and memory or storage capacity in bits; the approximation of probability distributions so that a maximal amount of information about the object being inferred is preserved; an information theoretic justification for multigrid methodology. The maximally informative field inference framework is described in full generality and denoted the Generalized Kalman Filter. The Generalized Kalman Filter allows the update of field knowledge from previous knowledge at any scale, and new data, to new knowledge at any other scale. An application example instance, the inference of continuous surfaces from measurements (for example, camera image data), is presented.

Keywords: bayesian inference; generalized Kalman filter; Kalman filter; Kullback-Leibler distance; maximally informative statistical inference; knowledge representation; minimum description length; sufficient statistics; multigrid methods; adaptive scale inference; adaptive grid inference; mutual information.