C
Creighton Heaukulani
Researcher at Singapore Ministry of Health
Publications - 18
Citations - 166
Creighton Heaukulani is an academic researcher from Singapore Ministry of Health. The author has contributed to research in topics: Negative binomial distribution & K-ary tree. The author has an hindex of 5, co-authored 17 publications receiving 130 citations. Previous affiliations of Creighton Heaukulani include University of Cambridge.
Papers
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Proceedings Article
Dynamic Probabilistic Models for Latent Feature Propagation in Social Networks
TL;DR: A new probabilistic model for capturing this phenomenon, which is called latent feature propagation, in social networks, is introduced and its capability for inferring such latent structure in varying types of social network datasets is demonstrated.
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The combinatorial structure of beta negative binomial processes
TL;DR: The combinatorial structure of conditionally-i.i.d. sequences of negative binomial processes with a common beta process base measure is characterized and the key Markov kernels needed to use a NB-IBP representation in a Markov Chain Monte Carlo algorithm targeting a posterior distribution are described.
Journal ArticleDOI
HOPES: An Integrative Digital Phenotyping Platform for Data Collection, Monitoring, and Machine Learning.
Xuancong Wang,Nikola Vouk,Creighton Heaukulani,Thisum Buddhika,Wijaya Martanto,Jimmy Lee,Robert J. T. Morris,Robert J. T. Morris +7 more
TL;DR: In this article, the authors describe the development and early experiences with a comprehensive digital phenotyping platform: Health Outcomes through Positive Engagement and Self-Empowerment (HOPES).
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Gibbs-type Indian buffet processes
TL;DR: In this article, a class of feature allocation models that generalize the Indian buffet process and are parameterized by Gibbs-type random measures is investigated. And the authors compare and contrast the utility of varying power-law behaviors in the latent features.
Proceedings Article
Scalable Bayesian dynamic covariance modeling with variational Wishart and inverse Wishart processes
TL;DR: In this paper, gradient-based variational inference routines for Wishart and inverse Wishart processes are implemented as Bayesian models for the dynamic, heteroskedastic covariance matrix of a multivariate time series.