  Bayesian methods for modelling and inference are being increasingly used in
the cryospheric sciences, and glaciology in particular. Here, we present a
review of recent works in glaciology that adopt a Bayesian approach when
conducting an analysis. We organise the chapter into three categories: i)
Gaussian-Gaussian models, ii) Bayesian hierarchical models, and iii) Bayesian
calibration approaches. In addition, we present two detailed case studies that
involve the application of Bayesian hierarchical models in glaciology. The
first case study is on the spatial prediction of surface mass balance across
the Icelandic mountain glacier Langj\"okull, and the second is on the
prediction of sea-level rise contributions from the Antactcic ice sheet. This
chapter is presented in such a way that it is accessible to both statisticians
as well as earth scientists.
