105 citations · 148 across the 18 of their papers we have counts for
7 papers · 1 filter
On a hypergraph probabilistic graphical model
Mohammad Ali Javidian, Linyuan Lu, Marco Valtorta +1
We propose a directed acyclic hypergraph framework for a probabilistic graphical model that we call Bayesian hypergraphs. The space of directed acyclic hypergraphs is much larger t…
Comment on: Decomposition of structural learning about directed acyclic graphs [1]
Mohammad Ali Javidian, Marco Valtorta
We propose an alternative proof concerning necessary and sufficient conditions to split the problem of searching for d-separators and building the skeleton of a DAG into small prob…
A Proof of the Front-Door Adjustment Formula
Mohammad Ali Javidian, Marco Valtorta
We provide a proof of the the Front-Door adjustment formula using the do-calculus.
Structural Learning of Multivariate Regression Chain Graphs via Decomposition
Mohammad Ali Javidian, Marco Valtorta
We extend the decomposition approach for learning Bayesian networks (BNs) proposed by (Xie et. al.) to learning multivariate regression chain graphs (MVR CGs), which include BNs as…
A New Result on the Complexity of Heuristic Estimates for the A* Algorithm
Othar Hansson, Andrew Mayer, Marco Valtorta
Relaxed models are abstract problem descriptions generated by ignoring constraints that are present in base-level problems. They play an important role in planning and search algor…
On the Properties of MVR Chain Graphs
Mohammad Ali Javidian, Marco Valtorta
Depending on the interpretation of the type of edges, a chain graph can represent different relations between variables and thereby independence models. Three interpretations, know…