| Notation | |||
|---|---|---|---|
| Parameters | (shape) | ||
| Support | is a positive-definite matrix with unit diagonal | ||
| Mean | the identity matrix | ||
In probability theory and Bayesian statistics, the Lewandowski-Kurowicka-Joe distribution, often referred to as the LKJ distribution, is a probability distribution over positive definite symmetric matrices with unit diagonals. [1]
The LKJ distribution was first introduced in 2009 in a more general context [2] by Daniel Lewandowski, Dorota Kurowicka, and Harry Joe. It is an example of the vine copula, an approach to constrained high-dimensional probability distributions.
The distribution has a single shape parameter and the probability density function for a matrix is
with normalizing constant , a complicated expression including a product over Beta functions. For , the distribution is uniform over the space of all correlation matrices; i.e. the space of positive definite matrices with unit diagonal.
The LKJ distribution is commonly used as a prior for correlation matrix in Bayesian hierarchical modeling. Bayesian hierarchical modeling often tries to make an inference on the covariance structure of the data, which can be decomposed into a scale vector and correlation matrix. [3] Instead of the prior on the covariance matrix such as the inverse-Wishart distribution, LKJ distribution can serve as a prior on the correlation matrix along with some suitable prior distribution on the scale vector. It has been implemented in several probabilistic programming languages, including Stan and PyMC.
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