41,893 research outputs found
Symbolic Calculus in Mathematical Statistics: A Review
In the last ten years, the employment of symbolic methods has substantially
extended both the theory and the applications of statistics and probability.
This survey reviews the development of a symbolic technique arising from
classical umbral calculus, as introduced by Rota and Taylor in The
usefulness of this symbolic technique is twofold. The first is to show how new
algebraic identities drive in discovering insights among topics apparently very
far from each other and related to probability and statistics. One of the main
tools is a formal generalization of the convolution of identical probability
distributions, which allows us to employ compound Poisson random variables in
various topics that are only somewhat interrelated. Having got a different and
deeper viewpoint, the second goal is to show how to set up algorithmic
processes performing efficiently algebraic calculations. In particular, the
challenge of finding these symbolic procedures should lead to a new method, and
it poses new problems involving both computational and conceptual issues.
Evidence of efficiency in applying this symbolic method will be shown within
statistical inference, parameter estimation, L\'evy processes, and, more
generally, problems involving multivariate functions. The symbolic
representation of Sheffer polynomial sequences allows us to carry out a
unifying theory of classical, Boolean and free cumulants. Recent connections
within random matrices have extended the applications of the symbolic method.Comment: 72 page
Connectionist Theory Refinement: Genetically Searching the Space of Network Topologies
An algorithm that learns from a set of examples should ideally be able to
exploit the available resources of (a) abundant computing power and (b)
domain-specific knowledge to improve its ability to generalize. Connectionist
theory-refinement systems, which use background knowledge to select a neural
network's topology and initial weights, have proven to be effective at
exploiting domain-specific knowledge; however, most do not exploit available
computing power. This weakness occurs because they lack the ability to refine
the topology of the neural networks they produce, thereby limiting
generalization, especially when given impoverished domain theories. We present
the REGENT algorithm which uses (a) domain-specific knowledge to help create an
initial population of knowledge-based neural networks and (b) genetic operators
of crossover and mutation (specifically designed for knowledge-based networks)
to continually search for better network topologies. Experiments on three
real-world domains indicate that our new algorithm is able to significantly
increase generalization compared to a standard connectionist theory-refinement
system, as well as our previous algorithm for growing knowledge-based networks.Comment: See http://www.jair.org/ for any accompanying file
On photon statistics parametrized by a non-central Wishart random matrix
In order to tackle parameter estimation of photocounting distributions,
polykays of acting intensities are proposed as a new tool for computing photon
statistics. As unbiased estimators of cumulants, polykays are computationally
feasible thanks to a symbolic method recently developed in dealing with
sequences of moments. This method includes the so-called method of moments for
random matrices and results to be particularly suited to deal with convolutions
or random summations of random vectors. The overall photocounting effect on a
deterministic number of pixels is introduced. A random number of pixels is also
considered. The role played by spectral statistics of random matrices is
highlighted in approximating the overall photocounting distribution when acting
intensities are modeled by a non-central Wishart random matrix. Generalized
complete Bell polynomials are used in order to compute joint moments and joint
cumulants of multivariate photocounters. Multivariate polykays can be
successfully employed in order to approximate the multivariate Mendel-Poisson
transform. Open problems are addressed at the end of the paper.Comment: 18 pages, in press in Journal of Statistical Planning and Inference,
201
Theoretical Interpretations and Applications of Radial Basis Function Networks
Medical applications usually used Radial Basis Function Networks just as Artificial Neural Networks. However, RBFNs are Knowledge-Based Networks that can be interpreted in several way: Artificial Neural Networks, Regularization Networks, Support Vector Machines, Wavelet Networks, Fuzzy Controllers, Kernel Estimators, Instanced-Based Learners. A survey of their interpretations and of their corresponding learning algorithms is provided as well as a brief survey on dynamic learning algorithms. RBFNs' interpretations can suggest applications that are particularly interesting in medical domains
On multivariable cumulant polynomial sequences with applications
A new family of polynomials, called cumulant polynomial sequence, and its
extensions to the multivariate case is introduced relied on a purely symbolic
combinatorial method. The coefficients of these polynomials are cumulants, but
depending on what is plugged in the indeterminates, either sequences of moments
either sequences of cumulants can be recovered. The main tool is a formal
generalization of random sums, also with a multivariate random index and not
necessarily integer-valued. Applications are given within parameter
estimations, L\'evy processes and random matrices and, more generally, problems
involving multivariate functions. The connection between exponential models and
multivariable Sheffer polynomial sequences offers a different viewpoint in
characterizing these models. Some open problems end the paper.Comment: 17 pages, In pres
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