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Linear Latent Force Models using Gaussian Processes
Gaussian Processes Linear Latent Force Models
2011/7/19
Purely data driven approaches for machine learning present difficulties when data is scarce relative to the complexity of the model or when the model is forced to extrapolate.
Decision Based Uncertainty Propagation Using Adaptive Gaussian Mixtures
Adaptive Gaussian Sum Decision Making
2011/7/19
Given a decision process based on the approximate probability density function returned by a data assimilation algorithm, an interaction level between the decision making level and the data assimilati...
Semi-Blind System Identification in Wireless Relay Networks via Gaussian Process Iterated Conditioning on the Modes Estimation
Relay networks System Identification Gaussian processes Kernel methods
2011/7/7
This paper presents a flexible stochastic model developed for a class of cooperative wireless relay networks, in which the relay processing functionality is not known at the destination. The challenge...
Variable Selection for Nonparametric Gaussian Process Priors: Models and Computational Strategies
Bayesian variable selection generalized linear models Gaussian processes
2011/7/5
This paper presents a unified treatment of Gaussian process models that extends to data from the exponential dispersion family and to survival data.
Intensive natural distribution as Bernoulli success ratio extension to continuous: enhanced Gaussian, continuous Poisson, and phenomena explanation
Intensive natural distribution as Bernoulli success ratio extension continuous Poisson phenomena explanation
2011/3/18
A new distribution called intensive natural distribution is introduced with the intent of merging statistics and empirical data. Based on the probability derived from the Bernoulli distribution, this ...
Making Tensor Factorizations Robust to Non-Gaussian Noise
Making Tensor Factorizations Robust Non-Gaussian Noise
2010/10/19
Tensors are multi-way arrays, and the Candecomp/Parafac (CP) tensor factorization has found application in many different domains. The CP model is typically fit using a least squares objective functio...