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Philip Deegan

dblp:222/2210 · DBLP profile ↗
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1ranked-venue papers
0as first author
0since 2021 · last 2017
—ORCID · none

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

Artificial intelligence and machine learning · 1

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.

Artificial intelligence
1 paper
Probabilistic and Bayesian machine learning · 100%
Interdisciplinary, comprehensive, and emerging computing
1 paper
Computational science and engineering · 100%

Topics — the 3 heaviest of 3, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Machine learning › Probabilistic and Bayesian machine learning › stochastic processes › point process › temporal point process
hawkes process		 0.312017
tick: a Python Library for Statistical Learning, with an emphasis on Hawkes Processes and Time-Dependent Models · J. Mach. Learn. Res. 2017
Machine learning › Probabilistic and Bayesian machine learning › stochastic processes
point process		 0.312017
tick: a Python Library for Statistical Learning, with an emphasis on Hawkes Processes and Time-Dependent Models · J. Mach. Learn. Res. 2017
Computational science and engineering › statistical computing
statistical software		 0.312017
tick: a Python Library for Statistical Learning, with an emphasis on Hawkes Processes and Time-Dependent Models · J. Mach. Learn. Res. 2017

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

time-dependent models · 0.6statistical learning · 0.6
YearPublicationVenuePosition
2017 tick: a Python Library for Statistical Learning, with an emphasis on Hawkes Processes and Time-Dependent Models
Emmanuel Bacry, Martin Bompaire, Philip Deegan, Stéphane Gaïffas, Søren Poulsen
 J. Mach. Learn. Res.3