Humans do the tasks such as image analysis, speech processing, decision making in a real time situation ( for example driving in a congested road ) are very efficiently. Especially our modern computers can't do in a linear way because we can't generalize these tasks with the help of designing an algorithm.
Aided by developments in statistics and artificial intelligence, researchers have begun to apply the concepts of probability theory rigorously to
problems in biological perception and action. One striking observation from this work is that human observers behave as optimal Bayesian observers.This leads naturally to the idea that perception is a process of unconscious, probabilistic inference.
To use sensory information efficiently to make judgments and guide action in the world, the brain must represent and use information about uncertainty in its computations for perception and action.
The fundamental concept behind the Bayesian approach is that the information provided by a set of sensory data about the world is represented by a conditional probability density function over the set of unknown variables. We needs to sharpen the statistical concepts studied in our school days. he he.
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