65 citations · 156 across the 21 of their papers we have counts for
13 papers · 1 filter
On Exponential Random Graph Models with Dyadic Independence
Kayvan Sadeghi
We show that the only exponential random graph model with n nodal parameters, dyads being independent, and the natural assumption of permutation-equivariant nodal parametrization i…
Axiomatization of Interventional Probability Distributions
Kayvan Sadeghi, Terry Soo
Causal intervention is an essential tool in causal inference. It is axiomatized under the rules of do-calculus in the case of structure causal models. We provide simple axiomatizat…
Conditions and Assumptions for Constraint-based Causal Structure Learning
Kayvan Sadeghi, Terry Soo
We formalize constraint-based structure learning of the "true" causal graph from observed data when unobserved variables are also existent. We provide conditions for a "natural" fa…
On Finite Exchangeability and Conditional Independence
Kayvan Sadeghi
We study the independence structure of finitely exchangeable distributions over random vectors and random networks. In particular, we provide necessary and sufficient conditions fo…
Markov Properties of Discrete Determinantal Point Processes
Kayvan Sadeghi, Alessandro Rinaldo
Determinantal point processes (DPPs) are probabilistic models for repulsion. When used to represent the occurrence of random subsets of a finite base set, DPPs allow to model globa…
On Exchangeability in Network Models
Steffen L. Lauritzen, Alessandro Rinaldo, Kayvan Sadeghi
We derive representation theorems for exchangeable distributions on finite and infinite graphs using elementary arguments based on geometric and graph-theoretic concepts. Our resul…
Random Networks, Graphical Models, and Exchangeability
Steffen Lauritzen, Alessandro Rinaldo, Kayvan Sadeghi
We study conditional independence relationships for random networks and their interplay with exchangeability. We show that, for finitely exchangeable network models, the empirical…
Faithfulness of Probability Distributions and Graphs
Kayvan Sadeghi
A main question in graphical models and causal inference is whether, given a probability distribution (which is usually an underlying distribution of data), there is a graph (o…
Unifying Markov Properties for Graphical Models
Steffen Lauritzen, Kayvan Sadeghi
Several types of graphs with different conditional independence interpretations --- also known as Markov properties --- have been proposed and used in graphical models. In this pap…
Pairwise Markov properties for regression graphs
Kayvan Sadeghi, Nanny Wermuth
With a sequence of regressions, one may generate joint probability distributions. One starts with a joint, marginal distribution of context variables having possibly a concentratio…
Total positivity in Markov structures
Shaun Fallat, Steffen Lauritzen, Kayvan Sadeghi +3
We discuss properties of distributions that are multivariate totally positive of order two (MTP2) related to conditional independence. In particular, we show that any independence…
Statistical Models for Degree Distributions of Networks
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 labelle…
models for random hypergraphs with a given degree sequence
Despina Stasi, Kayvan Sadeghi, Alessandro Rinaldo +2
We introduce the beta model for random hypergraphs in order to represent the occurrence of multi-way interactions among agents in a social network. This model builds upon and gener…