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Kenric P. Nelson

dblp:49/6117 · DBLP profile ↗
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2ranked-venue papers
1as first author
1since 2021 · last 2021
0000-0001-6962-7459ORCID · corroborated

Domains — the database's venue-derived domains; a paper can count in several

Theory of computation · 2 · 1 first-author · 1 since 2021

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Theoretical computer science
2 papers
Information theory · 64% Algorithms and data structures · 34% Mathematical optimization · 1%

Topics — the 4 heaviest of 4, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Information theory › probability theory › continuous distributions
q-gaussian distribution
0.622021
Comments on "Generalized Box-Müller Method for Generating q-Gaussian Random Deviates" · IEEE Trans. Inf. Theory 2021
Generalized Box-Müller Method for Generating q-Gaussian Random Deviates · IEEE Trans. Inf. Theory 2007
Algorithms and data structures › randomized algorithms › sampling
random variate generation
0.622021
Comments on "Generalized Box-Müller Method for Generating q-Gaussian Random Deviates" · IEEE Trans. Inf. Theory 2021
Generalized Box-Müller Method for Generating q-Gaussian Random Deviates · IEEE Trans. Inf. Theory 2007
Information theory › probability theory
probability distributions
0.512021
Comments on "Generalized Box-Müller Method for Generating q-Gaussian Random Deviates" · IEEE Trans. Inf. Theory 2021
Mathematical optimization › metaheuristic optimization
simulated annealing
0.012007
Generalized Box-Müller Method for Generating q-Gaussian Random Deviates · IEEE Trans. Inf. Theory 2007

Methods — techniques the papers use, named apart from their topics

tail decay parameterization · 0.5generalized logarithm · 0.5q-entropy · 0.1box-müller method · 0.1
YearPublicationVenuePosition
2021 Comments on "Generalized Box-Müller Method for Generating q-Gaussian Random Deviates"
abstract
The generalized Box-Müller algorithm provides a methodology for generating q-Gaussian random variates, which generalizes the Gaussian$(q=1)$. The parameter$-\infty < {q}\le 3$is related to the shape of the tail decay;${q} < 1$for compact-support including parabola$\left ({{q}={\it{ 0}} }\right)$;$1 < {q}\le 3$for heavy-tail including Cauchy$\left ({{q}=2 }\right)$. This addendum clarifies the transformation${q}^{\prime }=\frac {3{q}-1}{{q}+1}$within the algorithm is due to a difference in the dimensions d of the generalized logarithm and the generalized distribution. The transformation is clarified by the decomposition of${q}=1+\frac {2\kappa }{1+{d}\kappa }$, where the shape parameter$-1 < \kappa \le \infty $quantifies the magnitude$\vphantom {^{R}}$of the deformation from exponential. A simpler specification for the generalized Box-Müller algorithm is provided using the shape of the tail decay.
Kenric P. Nelson, William Thistleton
IEEE Trans. Inf. Theory1
2007 Generalized Box-Müller Method for Generating q-Gaussian Random Deviates
abstract
The q-Gaussian distribution is known to be an attractor of certain correlated systems and is the distribution which, under appropriate constraints, maximizes a generalization of the familiar Shannon entropy. This generalized entropy, or q-entropy, provides the basis of nonextensive statistical mechanics, a theory which is postulated as a natural extension of the standard (Boltzmann-Gibbs) statistical mechanics, and which may explain the ubiquitous appearance of heavy-tailed distributions in both natural and man-made systems. The q-Gaussian distribution is also used as a numerical tool, for example as a visiting distribution in Generalized Simulated Annealing. A simple, easy to implement numerical method for generating random deviates from a q-Gaussian distribution based upon a generalization of the well known Box-Miiller method is developed and presented. This method is suitable for a larger range of q values, -infin < q < 3, than has previously appeared in the literature, and can generate deviates from q-Gaussian distributions of arbitrary width and center. MATLAB code showing a straightforward implementation is also included.
William Thistleton, John A. Marsh, Kenric P. Nelson, Constantino Tsallis
IEEE Trans. Inf. Theory3