26,032 research outputs found
Nonparametric Information Geometry
The differential-geometric structure of the set of positive densities on a
given measure space has raised the interest of many mathematicians after the
discovery by C.R. Rao of the geometric meaning of the Fisher information. Most
of the research is focused on parametric statistical models. In series of
papers by author and coworkers a particular version of the nonparametric case
has been discussed. It consists of a minimalistic structure modeled according
the theory of exponential families: given a reference density other densities
are represented by the centered log likelihood which is an element of an Orlicz
space. This mappings give a system of charts of a Banach manifold. It has been
observed that, while the construction is natural, the practical applicability
is limited by the technical difficulty to deal with such a class of Banach
spaces. It has been suggested recently to replace the exponential function with
other functions with similar behavior but polynomial growth at infinity in
order to obtain more tractable Banach spaces, e.g. Hilbert spaces. We give
first a review of our theory with special emphasis on the specific issues of
the infinite dimensional setting. In a second part we discuss two specific
topics, differential equations and the metric connection. The position of this
line of research with respect to other approaches is briefly discussed.Comment: Submitted for publication in the Proceedings od GSI2013 Aug 28-30
2013 Pari
Dual Connections in Nonparametric Classical Information Geometry
We construct an infinite-dimensional information manifold based on
exponential Orlicz spaces without using the notion of exponential convergence.
We then show that convex mixtures of probability densities lie on the same
connected component of this manifold, and characterize the class of densities
for which this mixture can be extended to an open segment containing the
extreme points. For this class, we define an infinite-dimensional analogue of
the mixture parallel transport and prove that it is dual to the exponential
parallel transport with respect to the Fisher information. We also define
{\alpha}-derivatives and prove that they are convex mixtures of the extremal
(\pm 1)-derivatives
Data expansion with Huffman codes
The following topics were dealt with: Shannon theory; universal lossless source coding; CDMA; turbo codes; broadband networks and protocols; signal processing and coding; coded modulation; information theory and applications; universal lossy source coding; algebraic geometry codes; modelling analysis and stability in networks; trellis structures and trellis decoding; channel capacity; recording channels; fading channels; convolutional codes; neural networks and learning; estimation; Gaussian channels; rate distortion theory; constrained channels; 2D channel coding; nonparametric estimation and classification; data compression; synchronisation and interference in communication systems; cyclic codes; signal detection; group codes; multiuser systems; entropy and noiseless source coding; dispersive channels and equalisation; block codes; cryptography; image processing; quantisation; random processes; wavelets; sequences for synchronisation; iterative decoding; optical communications
Coupling geometry on binary bipartite networks: hypotheses testing on pattern geometry and nestedness
Upon a matrix representation of a binary bipartite network, via the
permutation invariance, a coupling geometry is computed to approximate the
minimum energy macrostate of a network's system. Such a macrostate is supposed
to constitute the intrinsic structures of the system, so that the coupling
geometry should be taken as information contents, or even the nonparametric
minimum sufficient statistics of the network data. Then pertinent null and
alternative hypotheses, such as nestedness, are to be formulated according to
the macrostate. That is, any efficient testing statistic needs to be a function
of this coupling geometry. These conceptual architectures and mechanisms are by
and large still missing in community ecology literature, and rendered
misconceptions prevalent in this research area. Here the algorithmically
computed coupling geometry is shown consisting of deterministic multiscale
block patterns, which are framed by two marginal ultrametric trees on row and
column axes, and stochastic uniform randomness within each block found on the
finest scale. Functionally a series of increasingly larger ensembles of matrix
mimicries is derived by conforming to the multiscale block configurations. Here
matrix mimicking is meant to be subject to constraints of row and column sums
sequences. Based on such a series of ensembles, a profile of distributions
becomes a natural device for checking the validity of testing statistics or
structural indexes. An energy based index is used for testing whether network
data indeed contains structural geometry. A new version block-based nestedness
index is also proposed. Its validity is checked and compared with the existing
ones. A computing paradigm, called Data Mechanics, and its application on one
real data network are illustrated throughout the developments and discussions
in this paper
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