Computational Statistics and Machine Learning
The Computational Statistics and Machine Learning Section (formerly known as the Statistical Computing Section) supports interest in all aspects of the use of computational science and technology in modern statistical analysis.
Computational Statistics and Machine Learning address reasoning from data in the presence of uncertainty by making fundamental use of computational algorithms; they sit at the interface of Statistics, Computer Science and some areas of Applied Mathematics. The section supports interest in all aspects of the use of computational science and technology in modern statistical analysis.
The section are also interested in the methodological development of statistical and machine learning algorithms and their application to modern statistics and data science
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- Act as a national focus for statisticians involved in theoretical and practical aspects of computational statistics and machine learning.
- To foster interaction with related data-intensive scientific communities, e.g. information engineering, computer science, data science and machine learning.
- Promote a statistically rigorous approach to the development of models and algorithms within computational statistics and machine learning.
- Organise meetings for the promotion and dissemination of research and developments in statistical computing, computational statistics, and machine learning.
- Promote best practice and standards in the design and development of computing systems that support the work of statisticians and machine learners, both in terms of systems and software, for statistical processing, documentation and analysis of data.
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