no code implementations • 8 May 2023 • Kayvan Sadeghi, Terry Soo
We provide simple axiomatizations for families of probability distributions to be different types of interventional distributions.
no code implementations • 24 Mar 2021 • Kayvan Sadeghi, Terry Soo
We provide conditions for a "natural" family of constraint-based structure-learning algorithms that output graphs that are Markov equivalent to the causal graph.
no code implementations • 4 Oct 2018 • Kayvan Sadeghi, Alessandro Rinaldo
Determinantal point processes (DPPs) are probabilistic models for repulsion.
no code implementations • 14 Nov 2014 • Kayvan Sadeghi, Alessandro Rinaldo
We define and study the statistical models in exponential family form whose sufficient statistics are the degree distributions and the bi-degree distributions of undirected labelled simple graphs.
no code implementations • 28 May 2014 • Kayvan Sadeghi
For this purpose, we define the class of chain mixed graphs (CMGs) with three types of edges and, for this class, provide a separation criterion under which the class of CMGs is stable under marginalization and conditioning and contains the class of LWF CGs as its subclass.
no code implementations • 19 Oct 2011 • Kayvan Sadeghi
In this paper, we study classes of graphs with three types of edges that capture the modified independence structure of a directed acyclic graph (DAG) after marginalisation over unobserved variables and conditioning on selection variables using the $m$-separation criterion.
no code implementations • 27 Sep 2011 • Kayvan Sadeghi, Steffen Lauritzen
In this paper, we unify the Markov theory of a variety of different types of graphs used in graphical Markov models by introducing the class of loopless mixed graphs, and show that all independence models induced by $m$-separation on such graphs are compositional graphoids.