# Naive Inference viewed as Computation

- Chris Thornton,
*University of Sussex*

## Abstract

Use of Bayesian models to explain both high- and low-level
aspects of cognitive function promises better connections
between cognitive science and cognitive neuroscience. But
standing in the way are fundamental problems, such as the
computational intractability of Bayesian inference, and the
general difficulty of understanding how Bayesian calculation
can deal with structural representation. Getting around the
problem of intractability seems to involve devising
effective methods for approximating optimal inference. But
there is the alternative of simplifying the interpretation
of how inference arises. While the process is normally taken
to involve calculations over an implied joint distribution,
it is possible to view it more simply as data-driven
application of conditional assertions. This naive
interpretation has several advantages with regard to
tractability and representation. The paper formalizes the
model and demonstrates some of its virtues.

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