Constructing Graphical Models via the Focused Information Criterion
نویسندگان
چکیده
منابع مشابه
Constructing Graphical Models via the Focused Information Criterion
A focused information criterion is developed to estimate undirected graphical models where for each node in the graph a generalized linear model is put forward conditioned upon the other nodes in the graph. The proposed method selects a graph with a small estimated mean squared error for a user-specified focus, which is a function of the parameters in the generalized linear models, by selecting...
متن کاملA focused information criterion for graphical models
A new method for model selection for Gaussian Bayesian networks and Markov networks, with extensions towards ancestral graphs, is constructed to have good mean squared error properties. The method is based on the focused information criterion, and offers the possibility of fitting individualtailored models. The focus of the research, that is, the purpose of the model, directs the selection. It ...
متن کاملStructure learning using a focused information criterion in graphical models
A new method for model selection for Gaussian directed acyclic graphs (DAG) and Gaussian graphical models (GGM), with extensions towards ancestral graphs (AG), is constructed to have good prediction properties. The method is based on the focused information criterion, and offers the possibility of fitting individual tailored models. The focus of the research, that is, the purpose of the model, ...
متن کاملConstructing Software from Graphical Models
Graphical models have been widely used to describe complex statistical models and to communicate these models to people who do not have a statistical background. In this paper we discus the WinBUGS software package which is based on these two ideas. A declarative language, the BUGS language, has been developed to describe complex statistical models. This language is processed by a compiler to p...
متن کاملGraphical Models via Generalized Linear Models
Undirected graphical models, also known as Markov networks, enjoy popularity in a variety of applications. The popular instances of these models such as Gaussian Markov Random Fields (GMRFs), Ising models, and multinomial discrete models, however do not capture the characteristics of data in many settings. We introduce a new class of graphical models based on generalized linear models (GLMs) by...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: SSRN Electronic Journal
سال: 2014
ISSN: 1556-5068
DOI: 10.2139/ssrn.2419382