Editing Dirichlet distribution (section)

====The concentration parameter====
Dirichlet distributions are very often used as [[prior distribution]]s in [[Bayesian inference]].  The simplest and perhaps most common type of Dirichlet prior is the symmetric Dirichlet distribution, where all parameters are equal.  This corresponds to the case where you have no prior information to favor one component over any other.  As described above, the single value {{mvar|α}} to which all parameters are set is called the [[concentration parameter]].  If the sample space of the Dirichlet distribution is interpreted as a [[discrete probability distribution]], then intuitively the concentration parameter can be thought of as determining how "concentrated" the probability mass of the Dirichlet distribution to its center, leading to samples with mass dispersed almost equally among all components, i.e., with a value much less than 1, the mass will be highly concentrated in a few components, and all the rest will have almost no mass, and with a value much greater than 1, the mass will be dispersed almost equally among all the components.  See the article on the [[concentration parameter]] for further discussion.