A Bayesian approach to truncated data sets: An application to Malmquist
  bias in Supernova Cosmology
  Large scale astronomical surveys are going wider and deeper than ever before.
However, astronomers, cosmologists and theorists continue to face the perennial
issue that their data sets are often incomplete in magnitude space and must be
carefully treated in order to avoid Malmquist bias, especially in the field of
supernova cosmology. Historically, cosmological parameter inference in
supernova cosmology was done using $\chi^2$ methodology; however, recent years
have seen a rise in the use of Bayesian Hierarchical Models. In this paper we
develop a Bayesian Hierarchical methodology to account for magnitude limited
surveys and present a specific application to cosmological parameter inference
and model selection in supernova cosmology.
