Lloyd T. Elliott

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Knowledge about the principles that govern large-scale neural representations of objects is central to a systematic understanding of object recognition. We used functional magnetic resonance imaging (fMRI) and multivariate pattern classification to investigate two such candidate principles: category preference and location encoding. The former designates(More)
Local voxel patterns of fMRI signals contain specific information about cognitive processes ranging from basic sensory processing to high level decision making. These patterns can be detected using multivariate pattern classification, and localization of these patterns can be achieved with searchlight methods in which the information content of spherical(More)
We propose a novel class of Bayesian nonparametric models for sequential data called fragmentation-coagulation processes (FCPs). FCPs model a set of sequences using a partition-valued Markov process which evolves by splitting and merging clusters. An FCP is exchangeable, projective, stationary and reversible, and its equilibrium distributions are given by(More)
We present a Bayesian nonparametric model for genetic sequence data in which a set of genetic sequences is modelled using a Markov model of partitions. The partitions at consecutive locations in the genome are related by the splitting and merging of their clusters. Our model can be thought of as a discrete analogue of the continuous(More)
Genetic sequence data are well described by hidden Markov models (HMMs) in which latent states correspond to clusters of similar mutation patterns. Theory from statistical genetics suggests that these HMMs are nonhomogeneous (their transition probabilities vary along the chromosome) and have large support for self transitions. We develop a new nonparametric(More)
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