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Objective Bayesian Model Selection

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Statistical Evaluation of Biometric Evidence in Forensic Automatic Speaker Recognition

Andrzej Drygajlo

Forensic speaker recognition is the process of determining if a specific individual (suspected speaker) is the source of a questioned voice recording (trace). This paper aims at presenting forensic automatic speaker recognition (FASR) methods that provide ...

Springer-Verlag New York, Ms Ingrid Cunningham, 175 Fifth Ave, New York, Ny 10010 Usa2009

Alignment of Multiple Configurations Using Hierarchical Models

Yann Ruffieux

We describe a method for aligning multiple unlabeled configurations simultane- ously. Specifically, we extend the two-configuration matching approach of Green and Mardia (2006) to the multiple configuration setting. Our approach is based on the in- troduct ...

2009

A systematic approach to model validation based on Bayesian updates and prediction related rejection criteria

Fabio Nobile

This work describes a solution to the validation challenge problem posed at the SANDIA Validation Challenge Workshop, May 21-23, 2006, NM. It presents and applies a general methodology to it. The solution entails several standard steps, namely selecting an ...

2008

The Banff challenge: Statistical detection of a noisy signal

Anthony Christopher Davison

Particle physics experiments such as those run in the Large Hadron Collider result in huge quantities of data, which are boiled down to a few numbers from which it is hoped that a signal will be detected. We discuss a simple probability model for this and ...

2008

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 for Spiking Neuron Models with a Sparsity Prior

Matthias Seeger, Matthias Bethge

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 ...

2008

Out-of-body experiences: false climbs in a supine position?

Olaf Blanke

Out-of-body experiences (OBEs) are illusions, where people experience themselves as being located outside their physical body and often flying or floating at an elevated location. Here, we propose that the flying and floating in OBEs can be explained as th ...

Ieee Service Center, 445 Hoes Lane, Po Box 1331, Piscataway, Nj 08855-1331 Usa2008

A mixture model for multivariate extremes

Anthony Christopher Davison, Marc-Olivier Boldi

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 ...

2007

Supervised Learning from the Bayesian Viewpoint: An informal overview

Claudio Mattiussi

This is the second episode of the Bayesian saga started with the tutorial on the Bayesian probability. Its aim is showing in very informal terms how supervised learning can be interpreted from the Bayesian viewpoint. The focus is put on supervised learning ...

2006

Efficient Nonparametric Bayesian Modelling with Sparse Gaussian Process Approximations

Matthias Seeger

Sparse approximations to Bayesian inference for nonparametric Gaussian Process models scale linearly in the number of training points, allowing for the application of powerful kernel-based models to large datasets. We present a general framework based on t ...

2006