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BMCBI
2010
147views more  BMCBI 2010»
13 years 5 months ago
baySeq: Empirical Bayesian methods for identifying differential expression in sequence count data
Background: High throughput sequencing has become an important technology for studying expression levels in many types of genomic, and particularly transcriptomic, data. One key w...
Thomas J. Hardcastle, Krystyna A. Kelly
UAI
2008
13 years 7 months ago
Refractor Importance Sampling
In this paper we introduce Refractor Importance Sampling (RIS), an improvement to reduce error variance in Bayesian network importance sampling propagation under evidential reason...
Haohai Yu, Robert van Engelen
JAMDS
2000
109views more  JAMDS 2000»
13 years 5 months ago
Robust estimation in Capital Asset Pricing Model
Bian and Dickey (1996) developed a robust Bayesian estimator for the vector of regression coefficients using a Cauchy-type g-prior. This estimator is an adaptive weighted average o...
Wing-Keung Wong, Guorui Bian
ICML
2009
IEEE
14 years 6 months ago
Structure learning of Bayesian networks using constraints
This paper addresses exact learning of Bayesian network structure from data and expert's knowledge based on score functions that are decomposable. First, it describes useful ...
Cassio Polpo de Campos, Zhi Zeng, Qiang Ji
ICANN
2010
Springer
13 years 6 months ago
Empirical Analysis of the Divergence of Gibbs Sampling Based Learning Algorithms for Restricted Boltzmann Machines
Abstract. Learning algorithms relying on Gibbs sampling based stochastic approximations of the log-likelihood gradient have become a common way to train Restricted Boltzmann Machin...
Asja Fischer, Christian Igel