Bayesian perceptual inference in linear Gaussian models
Author(s)
Battaglia, Peter W.![Thumbnail](/bitstream/handle/1721.1/58669/MIT-CSAIL-TR-2010-046.pdf.jpg?sequence=5&isAllowed=y)
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Other Contributors
Computational Cognitive Science
Advisor
Joshua Tenenbaum
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The aim of this paper is to provide perceptual scientists with a quantitative framework for modeling a variety of common perceptual behaviors, and to unify various perceptual inference tasks by exposing their common computational underpinnings. This paper derives a model Bayesian observer for perceptual contexts with linear Gaussian generative processes. I demonstrate the relationship between four fundamental perceptual situations by expressing their corresponding posterior distributions as consequences of the model's predictions under their respective assumptions.
Date issued
2010-09-21Series/Report no.
MIT-CSAIL-TR-2010-046
Keywords
cue integration, cue combination, explaining away, discounting
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