For over 200 years, philosophers and mathematicians have been using probability theory to describe human cognition. While the theory of probabilities was first developed as a means of analyzing games of chance, it quickly took on a larger and deeper significance as a formal account of how rational agents should reason in situations of uncertainty

There has been an explosive growth of data-mining models involving latent structure for clustering and classification. While having related objectives these models use different parameterizations and often very different specifications and constraints. Model choice is thus a major methodological issue and a crucial practical one for applications. In this paper, we work from a general formulation of hierarchical Bayesian mixed-membership models in Erosheva [15] and Erosheva, Fienberg, and Lafferty [19] and present several model specifications and variations, both parametric and nonparametric, in the context of the learning the number of latent groups and associated patterns for clustering units. Model choice is an issue within specifications, and becomes a component of the larger issue of model comparison. We elucidate strategies for comparing models and specifications by producing novel analyses of two data sets: (1) a corpus of scientific publications from the Proceedings of the National Academy of Sciences (PNAS) examined earlier by Erosheva, Fienberg, and Lafferty [19] and Griffiths and Steyvers [22]; (2) data on functionally disabled American seniors from the National

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Abstract — For robots operating in real-world environments, the ability to deal with dynamic entities such as humans, animals, vehicles, or other robots is of fundamental importance. The variability of dynamic objects, however, is large in general, which makes it hard to manually design suitable models for their appearance and dynamics. In this paper, we present an unsupervised learning approach to this model-building problem. We describe an exemplar-based model for representing the time-varying appearance of objects in planar laser scans as well as a clustering procedure that builds a set of object classes from given training sequences. Extensive experiments in real environments demonstrate that our system is able to autonomously learn useful models for, e.g., pedestrians, skaters, or cyclists without being provided with external class information. I.

The aim of my PhD is to develop novel algorithms to allow an Autonomous Underwater Vehicle (AUV) to locate hydrothermal vents on the ocean floor. Hydrothermal vents are tectonically-driven outgassings of mineral-rich superheated water, and they produce a chemical-advecting plume that can be detected from kilometres away. Finding vents is challenging firstly because detecting a chemical tracer from a plume gives very little information on the bearing or range to the source, and secondly because tracers from different vents combine in an additive way, and there is no a priori way of telling how many vents have contributed to a measured signal. I have decomposed the task of finding vents into a mapping problem, where a probabilistic map of nearby vents is constructed, and a planning problem, which uses the uncertain map to determine actions the AUV should take to allow it to find as many vents as possible on a mission, subject to the limited power resources it has. Both problems will require the devel-opment of new methods to solve them. The mapping problem is novel because sensors do not provide even an approximate range to their target, there are potentially multiple targets, and

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Graduate School for originality

This paper presents a study on the potential interest of sparse Independent Component Analysis (ICA) for the diagnosis of a complex railway infrastructure device. This complex system is composed of several spatially related subsystems, i.e. a defective subsystem not only modifies its own inspection data but also those of other subsystems. In this context, the ICA model is used to extract from inspection data indicators of each subsystem state. We assume here that inspection data are observed variables generated by a linear mixture of independent and nongaussian latent variables linked to the defects. Furthermore, physical knowledge on the inspection system provides prior information on the mixing structure. We investigate then the ability of sparse ICA to recover this structure and to provide meaningful defect indicators. We also show that introducing sparsity in the mixing process slightly improves the results. 1.

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