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» On Bayesian model and variable selection using MCMC
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BMCBI
2007
138views more  BMCBI 2007»
13 years 5 months ago
A novel Bayesian approach to quantify clinical variables and to determine their spectroscopic counterparts in 1H NMR metabonomic
Background: A key challenge in metabonomics is to uncover quantitative associations between multidimensional spectroscopic data and biochemical measures used for disease risk asse...
Aki Vehtari, Ville-Petteri Mäkinen, Pasi Soin...
UAI
1996
13 years 6 months ago
Asymptotic Model Selection for Directed Networks with Hidden Variables
We extend the Bayesian Information Criterion (BIC), an asymptotic approximation for the marginal likelihood, to Bayesian networks with hidden variables. This approximation can be ...
Dan Geiger, David Heckerman, Christopher Meek
IJON
2008
123views more  IJON 2008»
13 years 5 months ago
Bayesian estimation of time delays between unevenly sampled signals
A method for estimating time delays between signals that are irregularly sampled is presented. The approach is based on postulating a latent variable model from which the observed...
Markus Harva, Somak Raychaudhury
ICASSP
2011
IEEE
12 years 9 months ago
MCMC inference of the shape and variability of time-response signals
Signals in response to time-localized events of a common phenomenon tend to exhibit a common shape, but with variable time scale, amplitude, and delay across trials in many domain...
Dmitriy A. Katz-Rogozhnikov, Kush R. Varshney, Ale...
ICASSP
2011
IEEE
12 years 9 months ago
A reversible jump MCMC algorithm for Bayesian curve fitting by using smooth transition regression models
This paper proposes a Bayesian algorithm to estimate the parameters of a smooth transition regression model. With in this model, time series are divided into segments and a linear...
Matthieu Sanquer, Florent Chatelain, Mabrouka El-G...