Zobrazeno 1 - 10
of 20
pro vyhledávání: '"Paul Bilokon"'
Autor:
Méabh MacMahon, Woochang Hwang, Soorin Yim, Eoghan MacMahon, Alexandre Abraham, Justin Barton, Mukunthan Tharmakulasingam, Paul Bilokon, Vasanthi Priyadarshini Gaddi, Namshik Han
Publikováno v:
Informatics in Medicine Unlocked, Vol 43, Iss , Pp 101387- (2023)
Background: Drug repurposing provides an opportunity to redeploy drugs, which are already tested for safety or approved for use in humans, for the treatment of diseases distinct from their primary indication. For example, the repurposing of dexametha
Externí odkaz:
https://doaj.org/article/43294507f5c0413ca08596e23b167d74
Autor:
Paul Bilokon
Publikováno v:
Quantitative Finance. 23:553-555
Publikováno v:
Web of Science
The calculation of option Greeks is vital for risk management. Traditional pathwise and finite-difference methods work poorly for higher-order Greeks and options with discontinuous payoff functions. The Quasi-Monte Carlo-based conditional pathwise me
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::9fbbdf1d8103314c3747d03afac434ff
http://arxiv.org/abs/2209.11337
http://arxiv.org/abs/2209.11337
Autor:
Paul Bilokon, David Finkelstein
The principal component analysis (PCA) is a staple statistical and unsupervised machine learning technique in finance. The application of PCA in a financial setting is associated with several technical difficulties, such as numerical instability and
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::8f88c8c4a9359898f67f179cc1f837e2
http://arxiv.org/abs/2108.13072
http://arxiv.org/abs/2108.13072
Publikováno v:
SSRN Electronic Journal.
We provide a data-driven algorithm to classify market regimes for time series. We utilise the path signature, encoding time series into easy-to-describe objects, and provide a metric structure which establishes a connection between separation of regi
Publikováno v:
Machine Learning in Finance ISBN: 9783030410674
This chapter introduces Bayesian regression and shows how it extends many of the concepts in the previous chapter. We develop kernel based machine learning methods—specifically Gaussian process regression, an important class of Bayesian machine lea
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::6960850ab11bda59570834cacfec3503
https://doi.org/10.1007/978-3-030-41068-1_3
https://doi.org/10.1007/978-3-030-41068-1_3
Publikováno v:
Machine Learning in Finance ISBN: 9783030410674
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::dbfcd902187f461bf59cb8867340bb0c
https://doi.org/10.1007/978-3-030-41068-1_6
https://doi.org/10.1007/978-3-030-41068-1_6
Publikováno v:
Machine Learning in Finance ISBN: 9783030410674
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::bfe60337590d991af61490a94c23907d
https://doi.org/10.1007/978-3-030-41068-1_2
https://doi.org/10.1007/978-3-030-41068-1_2
Publikováno v:
Machine Learning in Finance ISBN: 9783030410674
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::4980db63e5412d6ea6ae64d2004287eb
https://doi.org/10.1007/978-3-030-41068-1_5
https://doi.org/10.1007/978-3-030-41068-1_5
Publikováno v:
Machine Learning in Finance ISBN: 9783030410674
This final chapter takes us forward to emerging research topics in quantitative finance and machine learning. Among many interesting emerging topics, we focus here on two broad themes. The first one deals with unification of supervised learning and r
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::8ceca8808d03b09ed8048efd42813346
https://doi.org/10.1007/978-3-030-41068-1_12
https://doi.org/10.1007/978-3-030-41068-1_12