  We compute accurate redshift distributions to I(AB) = 24 and R(AB) = 24.5
using photometric redshifts estimated from six-band UBVRIZ photometry in the
Canada-France Deep Fields-Photometric Redshift Survey (CFDF-PRS). Our
photometric redshift algorithm is calibrated using hundreds of CFRS
spectroscopic redshifts in the same fields. The dispersion in redshift is
\sigma/(1+z) \la 0.04 to the CFRS depth of I(AB) = 22.5, rising to \sigma/(1+z)
\la 0.06 at our nominal magnitude and redshift limits of I(AB) = 24 and z \le
1.3, respectively. We describe a new method to compute N(z) that incorporates
the full redshift likelihood functions in a Bayesian iterative analysis and we
demonstrate in extensive Monte Carlo simulations that it is superior to
distributions calculated using simple maximum likelihood redshifts. The
field-to-field differences in the redshift distributions, while not unexpected
theoretically, are substantial even on 30' scales. We provide I(AB) and R(AB)
redshift distributions, median redshifts, and parametrized fits of our results
in various magnitude ranges, accounting for both random and systematic errors
in the analysis.
