Výsledky vyhledávání - "Kent, John P."

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Autor: Mardia, Kanti V., Wu, Xiangyu, Kent, John T., Goodall, Colin R., Khambay, Balvinder S.

Many biological objects possess bilateral symmetry about a midline or midplane, up to a ``noise'' term. This paper uses landmark-based methods to measure departures from bilateral symmetry, especially for the two-group problem where one group is more

Externí odkaz: http://arxiv.org/abs/2407.17225

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Robust score matching for compositional data

Autor: Scealy, Janice L., Hingee, Kassel L., Kent, John T., Wood, Andrew T. A.

The restricted polynomially-tilted pairwise interaction (RPPI) distribution gives a flexible model for compositional data. It is particularly well-suited to situations where some of the marginal distributions of the components of a composition are co

Externí odkaz: http://arxiv.org/abs/2305.07349

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Directional distributions and the half-angle principle

Autor: Kent, John T.

Angle halving, or alternatively the reverse operation of angle doubling, is a useful tool when studying directional distributions. It is especially useful on the circle where, in particular, it yields an identification between the wrapped Cauchy dist

Externí odkaz: http://arxiv.org/abs/2202.06611

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Mixture models for spherical data with applications to protein bioinformatics

Autor: Mardia, Kanti V., Barber, Stuart, Burdett, Philippa M., Kent, John T., Hamelryck, Thomas

Finite mixture models are fitted to spherical data. Kent distributions are used for the components of the mixture because they allow considerable flexibility. Previous work on such mixtures has used an approximate maximum likelihood estimator for the

Externí odkaz: http://arxiv.org/abs/2104.13140

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Improved Digital Therapy for Developmental Pediatrics Using Domain-Specific Artificial Intelligence: Machine Learning Study

Autor: Washington, Peter, Kalantarian, Haik, Kent, John, Husic, Arman, Kline, Aaron, Leblanc, Emilie, Hou, Cathy, Mutlu, Onur Cezmi, Dunlap, Kaitlyn, Penev, Yordan, Varma, Maya, Stockham, Nate Tyler, Chrisman, Brianna, Paskov, Kelley, Sun, Min Woo, Jung, Jae-Yoon, Voss, Catalin, Haber, Nick, Wall, Dennis Paul

Publikováno v: JMIR pediatrics and parenting 5.2 (2022): e26760

Background: Automated emotion classification could aid those who struggle to recognize emotions, including children with developmental behavioral conditions such as autism. However, most computer vision emotion recognition models are trained on adult

Externí odkaz: http://arxiv.org/abs/2012.08678

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