Addendum on the scoring of Gaussian directed acyclic graphical models
We provide a correction to the expression for scoring Gaussian directed acyclic graphical models derived in Geiger and Heckerman [Ann. Statist. 30 (2002) 1414-1440] and discuss how to evaluate the score efficiently.
Code (0)
등록된 구현이 없습니다.
Similar Papers 제목 키워드 기반
Learning Linear Non-Gaussian Graphical Models with Multidirected Edges
In this paper we propose a new method to learn the underlying acyclic mixed graph of a linear non-Gaussian structural equation model given observational data. We build on an algorithm proposed by Wang and Drton, and we s…
Optimal estimation of Gaussian DAG models
We study the optimal sample complexity of learning a Gaussian directed acyclic graph (DAG) from observational data. Our main results establish the minimax optimal sample complexity for learning the structure of a linear …
Characterizing Distribution Equivalence and Structure Learning for Cyclic and Acyclic Directed Graphs
The main approach to defining equivalence among acyclic directed causal graphical models is based on the conditional independence relationships in the distributions that the causal models can generate, in terms of the Ma…
On perfectness in Gaussian graphical models
Knowing when a graphical model is perfect to a distribution is essential in order to relate separation in the graph to conditional independence in the distribution, and this is particularly important when performing infe…
DAG-GPs: Learning Directed Acyclic Graph Structure For Multi-Output Gaussian Processes
Multi-output Gaussian processes (MOGPs) introduce correlations between outputs, but are subject to negative transfer, where learned correlations associate an output with another that is actually unrelated, leading to dim…
Gaussian Processes