An algorithm for constructing high dimensional distributions from distributions of lower dimension

We propose a new sequential procedure for estimating multivariate distributions in cases when conventional maximum likelihood has too many parameters and is therefore inaccurate or non-operational. The procedure constructs a multivariate distribution and its pseudo-likelihood sequentially, in each step using lower-dimensional distributions with a small number of parameters. In an application, the procedure provides excellent fit when the dimension is moderate, and remains operational when the conventional method fails.