Bayesian linear regression


Bayesian linear regression is a form of linear regression that uses the information about the variances of the input variables (whose errors have to be normally distributed as a precondition to ordinary linear regression anyway) to produce a probability distribution for outputs. While ordinary linear regression predicts a single output value on the basis of a range of input values, Bayesian linear regression predicts a probable value (which will be close to the single output value that the ordinary algorithm would have predicted) and a standard distribution around that probable value.

In situations where the variances of input variables are known prior to training, this information can also be incorporated into the model.

has functional building block
FBB_Value prediction
has input data type
IDT_Vector of quantitative variables
has internal model
INM_Function INM_Probability
has output data type
ODT_Quantitative variable
has learning style
has parametricity
has relevance
sometimes supports
mathematically similar to