Discovery Projects - Grant ID: DP230100905

Funding Activity

Website
http://dataportal.arc.gov.au/NCGP/Web/Grant/Grant/DP230100905

Funding Status
Active

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Funded Activity Summary

Stochastic majorization--minimization algorithms for data science. The changing nature of acquisition and storage data has made the process of drawing inference infeasible with traditional statistical and machine learning methods. Modern data are often acquired in real time, in an incremental nature, and are often available in too large a volume to process on conventional machinery. The project proposes to study the family of stochastic majorisation-minimisation algorithms for computation of inferential quantities in an incremental manner. The proposed stochastic algorithms encompass and extend upon a wide variety of current algorithmic frameworks for fitting statistical and machine learning models, and can be used to produce feasible and practical algorithms for complex models, both current and future.

Funded Activity Details

Start Date: 01-01-2023

End Date: 31-12-2025

Funding Scheme: Discovery Projects

Funding Amount: $360,000.00

Funder: Australian Research Council

Research Topics

ANZSRC Field of Research (FoR)

Statistical data science | Optimisation | Computational statistics | Statistics

ANZSRC Socio-Economic Objective (SEO)

Other Keywords

Computational statistics | Expanding Knowledge In the Information and Computing Sciences | Expanding Knowledge In the Mathematical Sciences | Optimisation | Statistical data science | Statistics

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