Fast Variational Bayesian Inference for Non-Conjugate Matrix Factorization Models
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We consider the problem of ranging with Impulse Radio (IR) Ultra-WideBand (UWB) radio under weak Line Of Sight (LOS) environments and additive Gaussian noise. We use a Bayesian approach where the prior distribution of the channel follows the IEEE 802.15.4a ...
The sparse linear model has seen many successful applications in Statistics, Machine Learning, and Computational Biology, such as identification of gene regulatory networks from micro-array expression data. Prior work has either approximated Bayesian infer ...
This paper introduces a novel probabilistic activity modeling approach that mines recurrent sequential patterns from documents given as word-time occurrences. In this model, documents are represented as a mixture of sequential activity motifs (or topics) a ...
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This paper introduces a novel probabilistic activity modeling approach that mines recurrent sequential patterns from documents given as word-time occurrences. In this model, documents are represented as a mixture of sequential activity motifs (or topics) a ...
Idiap2010
A wide range of problems such as signal reconstruction, denoising, source separation, feature selection, and graphical model search are addressed today by posterior maximization for linear models with sparsity-favouring prior distributions. The Bayesian po ...
We show how variational Bayesian inference can be implemented for very large binary classification generalized linear models. Our relaxation is shown to be a convex problem for any log-concave model, and we provide an efficient double loop algorithm for so ...
The linear model with sparsity-favouring prior on the coefficients has important applications in many different domains. In machine learning, most methods to date search for maximum a posteriori sparse solutions and neglect to represent posterior uncertain ...
Massachusetts Institute of Technology Press2008
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The spectral density function plays a key role in fitting the tail of multivariate extremal data and so in estimating probabilities of rare events. This function satisfies moment constraints but unlike the univariate extreme value distributions has no simple ...
Generalized linear models are the most commonly used tools to describe the stimulus selectivity of sensory neurons. Here we present a Bayesian treatment of such models. Using the expectation propagation algorithm, we are able to approximate the full poster ...