2,887 research outputs found
Acceleration of generalized hypergeometric functions through precise remainder asymptotics
We express the asymptotics of the remainders of the partial sums {s_n} of the
generalized hypergeometric function q+1_F_q through an inverse power series z^n
n^l \sum_k c_k/n^k, where the exponent l and the asymptotic coefficients {c_k}
may be recursively computed to any desired order from the hypergeometric
parameters and argument. From this we derive a new series acceleration
technique that can be applied to any such function, even with complex
parameters and at the branch point z=1. For moderate parameters (up to
approximately ten) a C implementation at fixed precision is very effective at
computing these functions; for larger parameters an implementation in higher
than machine precision would be needed. Even for larger parameters, however,
our C implementation is able to correctly determine whether or not it has
converged; and when it converges, its estimate of its error is accurate.Comment: 36 pages, 6 figures, LaTeX2e. Fixed sign error in Eq. (2.28), added
several references, added comparison to other methods, and added discussion
of recursion stabilit
An investigation of messy genetic algorithms
Genetic algorithms (GAs) are search procedures based on the mechanics of natural selection and natural genetics. They combine the use of string codings or artificial chromosomes and populations with the selective and juxtapositional power of reproduction and recombination to motivate a surprisingly powerful search heuristic in many problems. Despite their empirical success, there has been a long standing objection to the use of GAs in arbitrarily difficult problems. A new approach was launched. Results to a 30-bit, order-three-deception problem were obtained using a new type of genetic algorithm called a messy genetic algorithm (mGAs). Messy genetic algorithms combine the use of variable-length strings, a two-phase selection scheme, and messy genetic operators to effect a solution to the fixed-coding problem of standard simple GAs. The results of the study of mGAs in problems with nonuniform subfunction scale and size are presented. The mGA approach is summarized, both its operation and the theory of its use. Experiments on problems of varying scale, varying building-block size, and combined varying scale and size are presented
From Relational Data to Graphs: Inferring Significant Links using Generalized Hypergeometric Ensembles
The inference of network topologies from relational data is an important
problem in data analysis. Exemplary applications include the reconstruction of
social ties from data on human interactions, the inference of gene
co-expression networks from DNA microarray data, or the learning of semantic
relationships based on co-occurrences of words in documents. Solving these
problems requires techniques to infer significant links in noisy relational
data. In this short paper, we propose a new statistical modeling framework to
address this challenge. It builds on generalized hypergeometric ensembles, a
class of generative stochastic models that give rise to analytically tractable
probability spaces of directed, multi-edge graphs. We show how this framework
can be used to assess the significance of links in noisy relational data. We
illustrate our method in two data sets capturing spatio-temporal proximity
relations between actors in a social system. The results show that our
analytical framework provides a new approach to infer significant links from
relational data, with interesting perspectives for the mining of data on social
systems.Comment: 10 pages, 8 figures, accepted at SocInfo201
Distribution of Gaussian Process Arc Lengths
We present the first treatment of the arc length of the Gaussian Process (GP)
with more than a single output dimension. GPs are commonly used for tasks such
as trajectory modelling, where path length is a crucial quantity of interest.
Previously, only paths in one dimension have been considered, with no
theoretical consideration of higher dimensional problems. We fill the gap in
the existing literature by deriving the moments of the arc length for a
stationary GP with multiple output dimensions. A new method is used to derive
the mean of a one-dimensional GP over a finite interval, by considering the
distribution of the arc length integrand. This technique is used to derive an
approximate distribution over the arc length of a vector valued GP in
by moment matching the distribution. Numerical simulations
confirm our theoretical derivations.Comment: 10 pages, 4 figures, Accepted to The 20th International Conference on
Artificial Intelligence and Statistics (AISTATS
The Multivariate Watson Distribution: Maximum-Likelihood Estimation and other Aspects
This paper studies fundamental aspects of modelling data using multivariate
Watson distributions. Although these distributions are natural for modelling
axially symmetric data (i.e., unit vectors where \pm \x are equivalent), for
high-dimensions using them can be difficult. Why so? Largely because for Watson
distributions even basic tasks such as maximum-likelihood are numerically
challenging. To tackle the numerical difficulties some approximations have been
derived---but these are either grossly inaccurate in high-dimensions
(\emph{Directional Statistics}, Mardia & Jupp. 2000) or when reasonably
accurate (\emph{J. Machine Learning Research, W. & C.P., v2}, Bijral \emph{et
al.}, 2007, pp. 35--42), they lack theoretical justification. We derive new
approximations to the maximum-likelihood estimates; our approximations are
theoretically well-defined, numerically accurate, and easy to compute. We build
on our parameter estimation and discuss mixture-modelling with Watson
distributions; here we uncover a hitherto unknown connection to the
"diametrical clustering" algorithm of Dhillon \emph{et al.}
(\emph{Bioinformatics}, 19(13), 2003, pp. 1612--1619).Comment: 24 pages; extensively updated numerical result
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