  Consider a 0-1 observation matrix M, where rows correspond to entities and
columns correspond to signals; a value of 1 (or 0) in cell (i,j) of M indicates
that signal j has been observed (or not observed) in entity i. Given such a
matrix we study the problem of inferring the underlying directed links between
entities (rows) and finding which entries in the matrix are initiators.
  We formally define this problem and propose an MCMC framework for estimating
the links and the initiators given the matrix of observations M. We also show
how this framework can be extended to incorporate a temporal aspect; instead of
considering a single observation matrix M we consider a sequence of observation
matrices M1,..., Mt over time.
  We show the connection between our problem and several problems studied in
the field of social-network analysis. We apply our method to paleontological
and ecological data and show that our algorithms work well in practice and give
reasonable results.
