Bayesian Estimation and Inference
Bayesian Estimation and Inference
This chapter addresses the problem of learning, and illustrates that when animals learn, they too appear to keep a measure of uncertainty about their estimates, and this uncertainty is a reflection of the history of the stimuli that they observed. It suggests that the brain combines information from various sensors if the sensors are reporting the consequences of a common event. This chapter shows that brain relies on prior experience to make predictions. It demonstrates that the motor system never believes that the small object weighs more than the larger one. It also mentions that Bayesian algorithms can solve the estimation problem.
Keywords: learning, brain, prior experience, motor system, Bayesian algorithms, estimation problem
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