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 "ƒ""X"("x" | "θ") of observable random variable "X" as a function of a parameter "θ". Then for a specific value "x" of "X", the function formula_1("θ" | "x") = "ƒ""X"("x" | "θ") is a likelihood function of "θ": it gives a measure of how "likely" any particular value of "θ" is, if we know that "X" has the value "x". The density function may be a density with respect to counting measure, i.e. a probability mass function.