1,686 research outputs found
Investigating Antigram Behaviour using Distributional Semantics
Language is an extremely interesting subject to study, each day presenting
new challenges and new topics for research. Words in particular have several
unique characteristics which when explored, prove to be astonishing. Anagrams
and Antigrams are such words possessing these amazing properties. The presented
work is an exploration into generating anagrams from a given word and
determining whether there exists antigram relationships between the pairs of
generated anagrams in light of the Word2Vec distributional semantic similarity
model. The experiments conducted, showed promising results for detecting
antigrams.Comment: 4 page
Coloring Sums of Extensions of Certain Graphs
Recall that the minimum number of colors that allow a proper coloring of
graph is called the chromatic number of and denoted by In
this paper the concepts of '-chromatic sum and -chromatic sum are
introduced. The extended graph of a graph was recently introduced for
certain regular graphs. We further the concepts of '-chromatic sum and
-chromatic sum to extended paths and cycles. The paper concludes with
\emph{patterned structured} graphs.Comment: 12 page
Consistent estimation of the spectrum of trace class data augmentation algorithms
Markov chain Monte Carlo is widely used in a variety of scientific
applications to generate approximate samples from intractable distributions. A
thorough understanding of the convergence and mixing properties of these Markov
chains can be obtained by studying the spectrum of the associated Markov
operator. While several methods to bound/estimate the second largest eigenvalue
are available in the literature, very few general techniques for consistent
estimation of the entire spectrum have been proposed. Existing methods for this
purpose require the Markov transition density to be available in closed form,
which is often not true in practice, especially in modern statistical
applications. In this paper, we propose a novel method to consistently estimate
the entire spectrum of a general class of Markov chains arising from a popular
and widely used statistical approach known as Data Augmentation. The transition
densities of these Markov chains can often only be expressed as intractable
integrals. We illustrate the applicability of our method using real and
simulated data.Comment: 43 pages (including Appendix), 3 figures; final versio
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