Bayesian statistical learning for big data biology

Research output: Contribution to journalReview article

Authors

Colleges, School and Institutes

External organisations

  • Institute of Cancer and Genomic Sciences, University of Birmingham, Birmingham, UK. c.yau@bham.ac.uk.
  • The Alan Turing Institute, London, UK. c.yau@bham.ac.uk.
  • Department of Molecular Oncology, BC Cancer Agency, Vancouver, Canada.

Abstract

Bayesian statistical learning provides a coherent probabilistic framework for modelling uncertainty in systems. This review describes the theoretical foundations underlying Bayesian statistics and outlines the computational frameworks for implementing Bayesian inference in practice. We then describe the use of Bayesian learning in single-cell biology for the analysis of high-dimensional, large data sets.

Details

Original languageEnglish
Pages (from-to)95-102
Number of pages8
JournalBiophysical Reviews
Volume11
Early online date7 Feb 2019
Publication statusE-pub ahead of print - 7 Feb 2019

Keywords

  • Bayesian, Computational biology, Statistical modelling