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8 papers · 2 filters
Generalized fiducial inference for normal linear mixed models
Jessi Cisewski, Jan Hannig
While linear mixed modeling methods are foundational concepts introduced in any statistical education, adequate general methods for interval estimation involving models with more t…
Causal Discovery of Linear Cyclic Models from Multiple Experimental Data Sets with Overlapping Variables
Antti Hyttinen, Frederick Eberhardt, Patrik O. Hoyer
Much of scientific data is collected as randomized experiments intervening on some and observing other variables of interest. Quite often, a given phenomenon is investigated in sev…
Robust Bayesian inference of networks using Dirichlet t-distributions
Michael Finegold, Mathias Drton
Bayesian graphical modeling provides an appealing way to obtain uncertainty estimates when inferring network structures, and much recent progress has been made for Gaussian models.…
Towards Characterizing Markov Equivalence Classes for Directed Acyclic Graphs with Latent Variables
Ayesha R. Ali, Thomas S. Richardson, Peter L. Spirtes +1
It is well known that there may be many causal explanations that are consistent with a given set of data. Recent work has been done to represent the common aspects of these explana…
The Nonparanormal SKEPTIC
Han Liu, Fang Han, Ming Yuan +2
We propose a semiparametric approach, named nonparanormal skeptic, for estimating high dimensional undirected graphical models. In terms of modeling, we consider the nonparanormal…
Sequential Nonparametric Regression
Haijie Gu, John Lafferty
We present algorithms for nonparametric regression in settings where the data are obtained sequentially. While traditional estimators select bandwidths that depend upon the sample…