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IROS
2008
IEEE
211views Robotics» more  IROS 2008»
14 years 4 months ago
GP-BayesFilters: Bayesian filtering using Gaussian process prediction and observation models
Abstract— Bayesian filtering is a general framework for recursively estimating the state of a dynamical system. The most common instantiations of Bayes filters are Kalman filt...
Jonathan Ko, Dieter Fox
NIPS
2008
13 years 11 months ago
Syntactic Topic Models
We develop the syntactic topic model (STM), a nonparametric Bayesian model of parsed documents. The STM generates words that are both thematically and syntactically constrained, w...
Jordan L. Boyd-Graber, David M. Blei
BMCBI
2010
110views more  BMCBI 2010»
13 years 10 months ago
A random effect multiplicative heteroscedastic model for bacterial growth
Background: Predictive microbiology develops mathematical models that can predict the growth rate of a microorganism population under a set of environmental conditions. Many prima...
Ricardo Cao, Mario Francisco-Fernández, Emi...
JMLR
2011
142views more  JMLR 2011»
13 years 5 months ago
Causal Search in Structural Vector Autoregressive Models
This paper reviews a class of methods to perform causal inference in the framework of a structural vector autoregressive model. We consider three different settings. In the first ...
Alessio Moneta, Nadine Chlass, Doris Entner, Patri...
SYNTHESE
2011
72views more  SYNTHESE 2011»
13 years 5 months ago
Science without (parametric) models: the case of bootstrap resampling
Scientific and statistical inferences build heavily on explicit, parametric models, and often with good reasons. However, the limited scope of parametric models and the increasin...
Jan Sprenger