Category:Principle of Parsimony

This category contains results about the Principle of Parsimony.

Let $P$ be a stochastic process which is being modelled by means of a stochastic model $M$.

$M$ will necessarily use a number of constants and parameters whose values are to be determined by estimation from the data.

The principle of parsimony dictates that $M$ employs the smallest possible number of parameters such that $M$ will adequately represent the behaviour of $P$.