Zobrazeno 1 - 10
of 271
pro vyhledávání: '"Geiger, Dan"'
Autor:
Heckerman, David, Geiger, Dan
We develop simple methods for constructing likelihoods and parameter priors for learning about the parameters and structure of a Bayesian network. In particular, we introduce several assumptions that permit the construction of likelihoods and paramet
Externí odkaz:
http://arxiv.org/abs/2105.06241
Autor:
Geiger, Dan, Heckerman, David
Publikováno v:
The Annals of Statistics, 30: 1412-1440, 2002
We develop simple methods for constructing parameter priors for model choice among Directed Acyclic Graphical (DAG) models. In particular, we introduce several assumptions that permit the construction of parameter priors for a large number of DAG mod
Externí odkaz:
http://arxiv.org/abs/2105.03248
Autor:
Geiger, Dan, Heckerman, David
We examine three probabilistic concepts related to the sentence "two variables have no bearing on each other". We explore the relationships between these three concepts and establish their relevance to the process of constructing similarity networks-
Externí odkaz:
http://arxiv.org/abs/1611.02126
Publikováno v:
Nature methods 12(4):332-334 (2015)
Linear mixed models (LMMs) have emerged as the method of choice for confounded genome-wide association studies. However, the performance of LMMs in non-randomly ascertained case-control studies deteriorates with increasing sample size. We propose a f
Externí odkaz:
http://arxiv.org/abs/1409.2448
We show how to find a minimum loop cutset in a Bayesian network with high probability. Finding such a loop cutset is the first step in Pearl's method of conditioning for inference. Our random algorithm for finding a loop cutset, called "Repeated WGue
Externí odkaz:
http://arxiv.org/abs/1408.1483
Autor:
Geiger, Dan, Shenoy, Prakash
This is the Proceedings of the Thirteenth Conference on Uncertainty in Artificial Intelligence, which was held in Providence, RI, August 1-3, 1997
Externí odkaz:
http://arxiv.org/abs/1304.3846
Autor:
Geiger, Dan, Pearl, Judea
This paper explores the role of Directed Acyclic Graphs (DAGs) as a representation of conditional independence relationships. We show that DAGs offer polynomially sound and complete inference mechanisms for inferring conditional independence relation
Externí odkaz:
http://arxiv.org/abs/1304.2355
An efficient algorithm is developed that identifies all independencies implied by the topology of a Bayesian network. Its correctness and maximality stems from the soundness and completeness of d-separation with respect to probability theory. The alg
Externí odkaz:
http://arxiv.org/abs/1304.1505
Autor:
Geiger, Dan, Heckerman, David
We examine three probabilistic formulations of the sentence a and b are totally unrelated with respect to a given set of variables U. First, two variables a and b are totally independent if they are independent given any value of any subset of the va
Externí odkaz:
http://arxiv.org/abs/1304.1145
Autor:
Geiger, Dan, Heckerman, David
This paper discuses multiple Bayesian networks representation paradigms for encoding asymmetric independence assertions. We offer three contributions: (1) an inference mechanism that makes explicit use of asymmetric independence to speed up computati
Externí odkaz:
http://arxiv.org/abs/1303.5718