2,772 research outputs found
Statistical Mechanics and Visual Signal Processing
The nervous system solves a wide variety of problems in signal processing. In
many cases the performance of the nervous system is so good that it apporaches
fundamental physical limits, such as the limits imposed by diffraction and
photon shot noise in vision. In this paper we show how to use the language of
statistical field theory to address and solve problems in signal processing,
that is problems in which one must estimate some aspect of the environment from
the data in an array of sensors. In the field theory formulation the optimal
estimator can be written as an expectation value in an ensemble where the input
data act as external field. Problems at low signal-to-noise ratio can be solved
in perturbation theory, while high signal-to-noise ratios are treated with a
saddle-point approximation. These ideas are illustrated in detail by an example
of visual motion estimation which is chosen to model a problem solved by the
fly's brain. In this problem the optimal estimator has a rich structure,
adapting to various parameters of the environment such as the mean-square
contrast and the correlation time of contrast fluctuations. This structure is
in qualitative accord with existing measurements on motion sensitive neurons in
the fly's brain, and we argue that the adaptive properties of the optimal
estimator may help resolve conlficts among different interpretations of these
data. Finally we propose some crucial direct tests of the adaptive behavior.Comment: 34pp, LaTeX, PUPT-143
Financial Applications of Random Matrix Theory: a short review
We discuss the applications of Random Matrix Theory in the context of
financial markets and econometric models, a topic about which a considerable
number of papers have been devoted to in the last decade. This mini-review is
intended to guide the reader through various theoretical results (the
Marcenko-Pastur spectrum and its various generalisations, random SVD, free
matrices, largest eigenvalue statistics, etc.) as well as some concrete
applications to portfolio optimisation and out-of-sample risk estimation.Comment: To appear in the "Handbook on Random Matrix Theory", Oxford
University Pres
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