Search Results for author: Kayvan Sadeghi

Found 7 papers, 0 papers with code

Axiomatization of Interventional Probability Distributions

no code implementations8 May 2023 Kayvan Sadeghi, Terry Soo

We provide simple axiomatizations for families of probability distributions to be different types of interventional distributions.

Causal Inference

Conditions and Assumptions for Constraint-based Causal Structure Learning

no code implementations24 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.

Markov Properties of Discrete Determinantal Point Processes

no code implementations4 Oct 2018 Kayvan Sadeghi, Alessandro Rinaldo

Determinantal point processes (DPPs) are probabilistic models for repulsion.

Point Processes

Statistical Models for Degree Distributions of Networks

no code implementations14 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.

Marginalization and Conditioning for LWF Chain Graphs

no code implementations28 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.

Stable mixed graphs

no code implementations19 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.

Markov properties for mixed graphs

no code implementations27 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.

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