Combining statistical models
Maria Sofia Massa, Steffen Lilholt Lauritzen · Contemporary mathematics - American Mathematical Society · 2010
This paper develops a general framework to support the combination of information from independent but related experiments, by introducing a formal way of combining statistical models represented by families of distributions.A typical example is the combination of multivariate Gaussian families respecting conditional independence constraints, i.e.Gaussian graphical models.Combining information from such models, represented by their dependence graphs, yields a formal basis for what could suitably be termed structural meta analysis.We consider issues of combination of pairs of distributions, extending the concept of meta-Markov combination introduced by Dawid and Lauritzen.The proposed theory is then applied to the special case of graphical models.