In [[probability theory]] and [[statistics]], a '''concentration parameter''' is a special kind of [[numerical parameter]] of a [[parametric family]] of [[probability distribution]]s. Concentration parameters occur in conjunction with distributions whose ___domain is a probability distribution, such as the symmetric [[symmetric Dirichlet distribution]] and the [[Dirichlet process]]. The larger the value of the concentration parameter, the more evenly distributed is the resulting distribution (the more it tends towards the [[uniform distribution]]). The smaller the value of the concentration parameter, the more sparsely distributed is the resulting distribution, with all but a few parameters having a probability near zero (in other words, the more it tends towards a distribution concentrated on a single point, termed the [[Dirac delta distribution]]). In the case of a Dirichlet distribution, a concentration parameter of 1 results in all sets of probabilities being equally likely, i.e. in this case the Dirichlet distribution of dimension <math>k</math> is equivalent to a uniform distribution over a <math>k</math>-dimensional simplex. Note that this is ''not'' the same as what happens when the concentration parameter tends towards infinity. In the former case, all resulting distributions are equally likely (the distribution over distributions is uniform). In the latter case, only near-uniform distributions are likely (the distribution over distributions is highly peaked around the uniform distribution). Meanwhile, in the limit as the concentration parameter tends towards zero, only distributions with nearly all mass concentrated on one of their components are likely (the distribution over distributions is highly peaked around the <math>k</math> possible [[Dirac delta distribution]]s centered on one of the components, or in terms of the <math>k</math>-dimensional simplex, is highly peaked at corners of the simplex).
