Bayesian statistical learning for big data biology
Research output: Contribution to journal › Review article › peer-review
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 language | English |
|---|---|
| Pages (from-to) | 95-102 |
| Number of pages | 8 |
| Journal | Biophysical Reviews |
| Volume | 11 |
| Early online date | 7 Feb 2019 |
| Publication status | E-pub ahead of print - 7 Feb 2019 |
Keywords
- Bayesian, Computational biology, Statistical modelling