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NIPS
2003
14 years 11 months ago
Ambiguous Model Learning Made Unambiguous with 1/f Priors
What happens to the optimal interpretation of noisy data when there exists more than one equally plausible interpretation of the data? In a Bayesian model-learning framework the a...
Gurinder S. Atwal, William Bialek
ICML
1999
IEEE
15 years 10 months ago
Monte Carlo Hidden Markov Models: Learning Non-Parametric Models of Partially Observable Stochastic Processes
We present a learning algorithm for non-parametric hidden Markov models with continuous state and observation spaces. All necessary probability densities are approximated using sa...
Sebastian Thrun, John Langford, Dieter Fox
CORR
2010
Springer
183views Education» more  CORR 2010»
14 years 8 months ago
Discovering shared and individual latent structure in multiple time series
This paper proposes a nonparametric Bayesian method for exploratory data analysis and feature construction in continuous time series. Our method focuses on understanding shared fe...
Suchi Saria, Daphne Koller, Anna Penn
96
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JMLR
2010
169views more  JMLR 2010»
14 years 4 months ago
Matrix-Variate Dirichlet Process Mixture Models
We are concerned with a multivariate response regression problem where the interest is in considering correlations both across response variates and across response samples. In th...
Zhihua Zhang, Guang Dai, Michael I. Jordan
CVPR
2008
IEEE
14 years 9 months ago
Tracking distributions with an overlap prior
Recent studies have shown that embedding similarity/dissimilarity measures between distributions in the variational level set framework can lead to effective object segmentation/t...
Ismail Ben Ayed, Shuo Li, Ian G. Ross