Directed acyclic graphical models are statistical models that use a directed acyclic graph (DAG) to represent the conditional independence structure of a set of random variables. We introduce a new class of DAG models tailored for circular variables having an Inverse Stereographic Normal distribution. We present a case study where the proposed models are employed to describe conditional independences of a sequence of angles summarising the structure of a protein.
Inverse Stereographic Gaussian DAG models / Anna Gottard; Agnese Panzera. - ELETTRONICO. - Italian Statistical Society Series on Advances in Statistics:(2025), pp. 136-141. (Intervento presentato al convegno SIS 2024) [10.1007/978-3-031-64447-4_23].
Inverse Stereographic Gaussian DAG models
Anna Gottard
;Agnese Panzera
2025
Abstract
Directed acyclic graphical models are statistical models that use a directed acyclic graph (DAG) to represent the conditional independence structure of a set of random variables. We introduce a new class of DAG models tailored for circular variables having an Inverse Stereographic Normal distribution. We present a case study where the proposed models are employed to describe conditional independences of a sequence of angles summarising the structure of a protein.
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