Bayesian Inference under Measurement Noise
The MIT Press eBooks · 2023
Bayesian Inference under Measurement NoiseHow does a Bayesian observer infer the state of the world from a noisy measurement?In chapter 1, we introduced the concept of inference through a variety of daily-life examples.In chapter 2, we examined how to calculate with Bayes' rule.However, the examples we used there involved inferences of categorical variables.The use of categorical variables makes Bayesian calculations easy, but in practice, many variables are continuous rather than categorical.World state variables that the brain may want to infer include the orientation of a line segment, the location of a sound source, the speed of a moving object, the color of a surface, or the time elapsed between two events.In this chapter, we discuss Bayesian models for such continuous variables.