In statistics, the likelihood principle is the proposition that, given a statistical model, all the evidence in a sample relevant to model parameters is contained in the likelihood function. A likelihood function arises from a probability density function considered as a function of its distributional parameterization argument. For example, consider a model which gives the probability density function of observable random variable as a function of a parameter Then for a specific value of the function is a likelihood function of it gives a measure of how "likely" any particular value of is, if we know that has the value The density function may be a density with respect to counting measure, i.e. a probability mass function.