Zobrazeno 1 - 10
of 45
pro vyhledávání: '"Ramsey, Joseph D."'
We give a novel nonparametric pointwise consistent statistical test (the Markov Checker) of the Markov condition for directed acyclic graph (DAG) or completed partially directed acyclic graph (CPDAG) models given a dataset. We also introduce the Cros
Externí odkaz:
http://arxiv.org/abs/2409.20187
Autor:
Ramsey, Joseph D., Andrews, Bryan
We give novel Python and R interfaces for the (Java) Tetrad project for causal modeling, search, and estimation. The Tetrad project is a mainstay in the literature, having been under consistent development for over 30 years. Some of its algorithms ar
Externí odkaz:
http://arxiv.org/abs/2308.07346
Autor:
Ramsey, Joseph D.
The Sparsest Permutation (SP) algorithm is accurate but limited to about 9 variables in practice; the Greedy Sparest Permutation (GSP) algorithm is faster but less weak theoretically. A compromise can be given, the Best Order Score Search, which give
Externí odkaz:
http://arxiv.org/abs/2108.10141
Standard fMRI connectivity analyses depend on aggregating the time series of individual voxels within regions of interest (ROIs). In certain cases, this spatial aggregation implies a loss of valuable functional and anatomical information about smalle
Externí odkaz:
http://arxiv.org/abs/1908.03264
Autor:
Ramsey, Joseph D., Andrews, Bryan
We compare Tetrad (Java) algorithms to the other public software packages BNT (Bayes Net Toolbox, Matlab), pcalg (R), bnlearn (R) on the \vanilla" task of recovering DAG structure to the extent possible from data generated recursively from linear, Ga
Externí odkaz:
http://arxiv.org/abs/1709.04240
Graphical causal models are an important tool for knowledge discovery because they can represent both the causal relations between variables and the multivariate probability distributions over the data. Once learned, causal graphs can be used for cla
Externí odkaz:
http://arxiv.org/abs/1704.02621
In this report we describe a tool for comparing the performance of graphical causal structure learning algorithms implemented in the TETRAD freeware suite of causal analysis methods. Currently the tool is available as package in the TETRAD source cod
Externí odkaz:
http://arxiv.org/abs/1607.08110
Autor:
Ramsey, Joseph D.
As standardly implemented in R or the Tetrad program, causal search algorithms used most widely or effectively by scientists have severe dimensionality constraints that make them inappropriate for big data problems without sacrificing accuracy. Howev
Externí odkaz:
http://arxiv.org/abs/1507.07749
Autor:
Ramsey, Joseph D.
Liu, et al., 2009 developed a transformation of a class of non-Gaussian univariate distributions into Gaussian distributions. Liu and collaborators (2012) subsequently applied the transform to search for graphical causal models for a number of empiri
Externí odkaz:
http://arxiv.org/abs/1505.01825
Autor:
Ramsey, Joseph D.
Many relations of scientific interest are nonlinear, and even in linear systems distributions are often non-Gaussian, for example in fMRI BOLD data. A class of search procedures for causal relations in high dimensional data relies on sample derived c
Externí odkaz:
http://arxiv.org/abs/1401.5031