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Gaussian Covariance and Scalable Variational Inference

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A Statistical Approach To The Inverse Problem In Magnetoencephalography

Zhigang Yao

Magnetoencephalography (MEG) is an imaging technique used to measure the magnetic field outside the human head produced by the electrical activity inside the brain. The MEG inverse problem, identifying the location of the electrical sources from the magnet ...

Inst Mathematical Statistics2014

Fast Dual Variational Inference for Non-Conjugate Latent Gaussian Models

Matthias Seeger, Mohammad Emtiyaz Khan

Latent Gaussian models (LGMs) are widely used in statistics and machine learning. Bayesian inference in non-conjugate LGMs is difficult due to intractable integrals in- volving the Gaussian prior and non-conjugate likelihoods. Algorithms based on variation ...

Omni Press2013

Cell Guidance on Nanogratings: A Computational Model of the Interplay between PC12 Growth Cones and Nanostructures

Silvestro Micera

Background: Recently, the effects of nanogratings have been investigated on PC12 with respect to cell polarity, neuronal differentiation, migration, maturation of focal adhesions and alignment of neurites. Methodology/Principal Findings: A synergistic proc ...

Public Library Science2013

Fast Variational Bayesian Inference for Non-Conjugate Matrix Factorization Models

Matthias Seeger

Probabilistic matrix factorization methods aim to extract meaningful correlation structure from an incomplete data matrix by postulating low rank constraints. Recently, variational Bayesian (VB) inference techniques have successfully been applied to such l ...

2012

Large Scale Bayesian Inference and Experimental Design for Sparse Linear Models

Matthias Seeger

Many problems of low-level computer vision and image processing, such as denoising, deconvolution, tomographic reconstruction or super-resolution, can be addressed by maximizing the posterior distribution of a sparse linear model (SLM). We show how higher- ...

2011

Variational Bayesian Inference Techniques

Matthias Seeger

Milestones in sparse signal reconstruction and compressive sensing can be understood in a probabilistic Bayesian context, fusing underdetermined measurements with knowledge about low level signal properties in the posterior distribution, which is maximized ...

Institute of Electrical and Electronics Engineers2010

Large Scale Bayesian Inference and Experimental Design for Sparse Linear Models

Matthias Seeger

2010

Bayesian Inference and Optimal Design in the Sparse Linear Model

Matthias Seeger

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

Bayesian Inference and Optimal Design in the Sparse Linear Model

Matthias Seeger

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 ...

2007

Multivariate Calibration of Non-replicated Measurements for Heteroscedastic Errors

Nirav Pravinbhai Bhatt

The quality of multivariate calibration (MVC) models obtained depends on the effective treatment of errors in spectral data. If errors in different absorbance measurements are correlated and have different variances, then the Maximum Likelihood Principal C ...

2007