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Browsing MACSI - Mathematics Application Consortium for Science & Industry by Author "O'Brien, Joseph D."

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Browsing MACSI - Mathematics Application Consortium for Science & Industry by Author "O'Brien, Joseph D."

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  • O'Brien, Joseph D.; Gleeson, James P. (Oxford University Press, 2021)
    A detailed analysis of matches played in the sport of Snooker during the period 1968–2020 is used to calculate a directed and weighted dominance network based upon the corresponding results. We consider a ranking procedure ...
  • O'Brien, Joseph D.; Burke, Kevin; Burke, Mark E.; Barmish, Ross B. (IEEE Computer Society, 2020)
    For sequential betting games, Kelly’s theory, aimed at maximization of the logarithmic growth of one’s account value, involves optimization of the so-called betting fraction K. In this Letter, we extend the classical ...
  • O'Brien, Joseph D.; Burke, Mark E.; Burke, Kevin (IEEE Computer Society, 2020)
    —We present a generalization of the Simultaneous Long-Short (SLS) trading strategy described in recent control literature wherein we allow for different parameters across the short and long sides of the controller; we refer ...
  • O'Brien, Joseph D.; Gleeson, James P.; O'Sullivan, David J.P. (Public Library of Science, 2021)
    In all competitions where results are based upon an individual’s performance the question of whether the outcome is a consequence of skill or luck arises. We explore this question through an analysis of a large dataset ...
  • O'Brien, Joseph D.; Aleta, Alberto; Moreno, Yamir; Gleeson, James P. (American Physical Society, 2020)
    The Hawkes process has garnered attention in recent years for its suitability to describe the behavior of online information cascades. Here we present a fully tractable approach to analytically describe the distribution ...
  • O'Brien, Joseph D.; Dassios, Ioannis K.; Gleeson, James P. (IOP Publishing, 2019)
    A model for the spreading of online information or ‘memes’ on multiplex networks is introduced and analyzed using branching-process methods. The model generalizes that of (Gleeson et al 2016 Phys. Rev.X) in two ways. ...

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