1,721,041 research outputs found
Exploring phase space with nested sampling
No full textWe present the first application of a Nested Sampling algorithm to explore the high-dimensional phase space of particle collision events. We describe the adaptation of the algorithm, designed to perform Bayesian inference computations, to the integration of partonic scattering cross sections and the generation of individual events distributed according to the corresponding squared matrix element. As a first concrete example we consider gluon scattering processes into 3-, 4- and 5-gluon final states and compare the performance with established sampling techniques. Starting from a flat prior distribution Nested Sampling outperforms the Vega
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Variations on the Author
Full text link“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Appropriate Similarity Measures for Author Cocitation Analysis
Full text linkWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
Dispelling the Myths Behind First-author Citation Counts
Full text linkWe conducted a full-scale evaluative citation analysis study of scholars in the XML research field to explore just how different from each other author rankings resulting from different citation counting methods actually are, and to demonstrate the capability of emerging data and tools on the Web in supporting more realistic citation counting methods. Our results contest some common arguments for the continued
use of first-author citation counts in the evaluation of scholars, such as high correlations between author rankings by first-author citation counts and other citation
counting methods, and high costs of using more realistic citation counting methods that are not well-supported by the ISI databases. It is argued that increasingly available digital full text research papers make it possible for citation analysis studies to go beyond what the ISI databases have directly supported and to employ more
sophisticated methods
koamabayili/VECTRON-author-checklist: VECTRON author checklist
No full textWe have done our best to complete the author checklist relating to the use of animals in the hut study. Note that the objective for the hut study was to evaluate the IRS treatment applications for residual efficacy against Anopheles mosquitoes, including the local An. coluzzii mosquito population. Cows were only used to attract mosquitoes into the huts and no tests were carried out directly on the cows. The author checklist is intended for use with studies where experiments are carried out on animals, which is why we have had such difficulty in completing this for the hut study, as many of the questions do not relate to how the cows were used
PolyChord: next generation nested sampling
No full textPaper references:
John Skilling. Nested sampling for general bayesian computation. Bayesian analysis, 1(4):833–859, 2006.
D. Sivia and J. Skilling. Data Analysis: A Bayesian Tutorial. Oxford science publications. OUP Oxford, 2006.
Feroz, Hobson, and Bridges. MULTINEST: an efficient and robust Bayesian inference tool for cosmology and particle physics. MNRAS, 398(4):1601–1614, Oct 2009.
F. Feroz and J. Skilling. Exploring multi-modal distributions with nested sampling. In American IoP Conference Series, volume 1553, pages 106–113, Aug 2013.
Michael Betancourt. Nested Sampling with Constrained Hamiltonian Monte Carlo. In American IofP Conference Series, volume 1305, pages 165–172, Mar 2011.
Adam Moss. Accelerated Bayesian inference using deep learning. arXiv e-prints, page arXiv:1903.10860, Mar 2019.
Joshua S Speagle. dynesty: A Dynamic Nested Sampling Package for Estimating Bayesian Posteriors and Evidences. arXiv e-prints, page arXiv:1904.02180, Apr 2019.
W. J. Handley, A. N. Lasenby, H. V. Peiris, and M. P. Hobson. Bayesian inflationary reconstructions from Planck 2018 data. PRD, 100(10):103511, Nov 2019.
Will Handley. Curvature tension: evidence for a closed universe. arXiv, 1908.09139, Aug 2019.
Hall, Thompson, Handley, and Queloz. On the Feasibility of Intense Radial Velocity Surveys for Earth-Twin Discoveries. MNRAS, 479(3):2968–2987, Sep 2018.
Gregory D. Martinez, James McKay, Ben Farmer, Pat Scott, Elinore Roebber, Antje Putze, and Jan Conrad. Comparison of statistical sampling methods with ScannerBit, the GAMBIT scanning module. European Physical Journal C, 77(11):761, Nov 2017.
Xi Chen, Farhan Feroz, and Michael Hobson. Bayesian automated posterior repartitioning for nested sampling. arXiv e-prints, page arXiv:1908.04655, Aug 2019.
W. Handley and J. Alsing. Compromise-free Likelihood free inference. Bayesian analysis (In preparation), 2020.
Will Handley. anesthetic: nested sampling visualisation. JOSS, 4:1414, May 2019.
E. Higson, W. Handley, L Hobson, and A Lasenby. Dynamic nested sampling. Statistics and Computation, 29(5):891–913, Sep 2019.
Brendon J. Brewer and Daniel Foreman-Mackey. DNest4: Diffusive Nested Sampling in C++ and Python. arXiv e-prints, page arXiv:1606.03757, Jun 2016.
Stefano Martiniani, Jacob D Stevenson, David J Wales, and Daan Frenkel. Superposition enhanced nested sampling. Physical Review X, 4(3):031034, 2014.
Philip Graff, Farhan Feroz, Michael P. Hobson, and Anthony Lasenby. BAMBI: blind accelerated multimodal Bayesian inference. MNRAS, 421(1):169–180, Mar 2012.
W. J. Handley, M. P. Hobson, and A. N. Lasenby. polychord: nested sampling for cosmology. MNRAS, 450:L61–L65, Jun 2015.
W. J. Handley, M. P. Hobson, and A. N. Lasenby. POLYCHORD: next-generation nested sampling. MNRAS, 453(4):4384–4398, Nov 2015.
K. Javid, W. J. Handley, M. P. Hobson, and L. Lasenby. Compromise-free Bayesian neural networks. Bayesian analysis (In preparation), 2020.
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