# Bayesian Statistical Decision Theory

A Bayesian Statistical Decision Theory is a decision theory/computational theory of Bayesian decision algorithms.

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**Counter-Example(s):****See:**Approximate Decision Task, Decision Processes.

## References

### 2004

- (Kersten et al., 2004) ⇒ Daniel Kersten, Pascal Mamassian, and Alan Yuille. (2004). “Object Perception As Bayesian Inference.” In: Annu. Rev. Psychol. Journal, 55. doi:10.1146/annurev.psych.55.090902.142005
- QUOTE: We perceive the shapes and material properties of objects quickly and reliably despite the complexity and objective ambiguities of natural images. Typical images are highly complex because they consist of many objects embedded in background clutter. Moreover, the image features of an object are extremely variable and ambiguous owing to the effects of projection, occlusion, background clutter, and illumination. The very success of everyday vision implies neural mechanisms, yet to be understood, that discount irrelevant information and organize ambiguous or noisy local image features into objects and surfaces. Recent work in Bayesian theories of visual perception has shown how complexity may be managed and ambiguity resolved through the task-dependent, probabilistic integration of prior object knowledge with image features. …
… Bayesian statistical decision theory formalizes Helmholtz’s idea of perception as inference

^{[1]}. Theoretical observers that use Bayesian inference to make optimal interpretations are called ideal observers. …… In this section, we discuss relating Bayesian decision theory to current theories of machine learning, learning the probability distributions relevant for vision, and determining algorithms for Bayesian inference. …

… The Bayesian approach seems completely different from the type of feed-forward models required to recognize objects in 150 ms (VanRullen & Thorpe 2001). How does the Bayesian approach relate to alternative models based on neural networks, radial basis functions, or other techniques?

- QUOTE: We perceive the shapes and material properties of objects quickly and reliably despite the complexity and objective ambiguities of natural images. Typical images are highly complex because they consist of many objects embedded in background clutter. Moreover, the image features of an object are extremely variable and ambiguous owing to the effects of projection, occlusion, background clutter, and illumination. The very success of everyday vision implies neural mechanisms, yet to be understood, that discount irrelevant information and organize ambiguous or noisy local image features into objects and surfaces. Recent work in Bayesian theories of visual perception has shown how complexity may be managed and ambiguity resolved through the task-dependent, probabilistic integration of prior object knowledge with image features. …