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.

Citation information

Yau, C. & Campbell, K. Biophys Rev (2019). https://doi.org/10.1007/s12551-019-00499-1

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