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We introduce a metric for probability distributions, which is bounded, information-theoretically motivated, and has a natural Bayesian interpretation. The square root of the well-known /spl chi//sup 2/ distance is an asymptotic approximation to it. Moreover, it is a close relative of the capacitory discrimination and Jensen-Shannon divergence.
Probabilistic systems for image analysis have enjoyed increasing popularity within the last few decades, yet principled approaches to incorporating occlusion as a feature into such systems are still few [11,7,8]. We present an approach which is strongly influenced by the work on noisy-or generative factor models (see e.g. [3]). We show how the(More)
We present an exact Bayesian treatment of a simple, yet sufficiently general probability distribution model. We consider piecewise-constant distributions P(X) with uniform (second-order) prior over location of discontinuity points and assigned chances. The predictive distribution and the model complexity can be determined completely from the data in a(More)
Building on our previous work [Taubert et al. 2012], we present an approach for real-time interaction between a real human and an avatar. We generate reactive motions by a dynamical extension of a hierarchical Gaussian process latent variable model, including latent dimensions for emotional style variation and target positions. This allows the avatar to(More)
The peristimulus time histogram (PSTH) and its more continuous cousin, the spike density function (SDF) are staples in the analytic toolkit of neurophysiol-ogists. The former is usually obtained by binning spike trains, whereas the standard method for the latter is smoothing with a Gaussian kernel. Selection of a bin width or a kernel size is often done in(More)
The peristimulus time histogram (PSTH) and its more continuous cousin, the spike density function (SDF) are staples in the analytic toolkit of neurophysiologists. The former is usually obtained by binning spike trains, whereas the standard method for the latter is smoothing with a Gaussian kernel. Selection of a bin width or a kernel size is often done in(More)