  We present a likelihood approach to modeling multi-dimensional GRB
Epeak--fluence--redshift data that naturally incorporates instrument detection
thresholds. The treatment of instrument thresholds is essential for analyzing
evidence for GRB evolution. The method described here compares the data to a
uniform jet model, in which the jet parameters are allowed to vary with
redshift. Data from different experiments may be modeled jointly. In addition,
BATSE data (for which no redshift information is available) may be incorporated
by ascribing to each event a likelihood derived from the full model by
integrating the probability density over the unknown redshift. The loss of
redshift information is mitigated by the large number of available bursts. We
discuss the implementation of the method, and validation of it using simulated
data.
