  We present ZEBRA, the Zurich Extragalactic Bayesian Redshift Analyzer. The
current version of ZEBRA combines and extends several of the classical
approaches to produce accurate photometric redshifts down to faint magnitudes.
In particular, ZEBRA uses the template-fitting approach to produce Maximum
Likelihood and Bayesian redshift estimates based on: (1.) An automatic
iterative technique to correct the original set of galaxy templates to best
represent the SEDs of real galaxies at different redshifts; (2.) A training set
of spectroscopic redshifts for a small fraction of the photometric sample; and
(3.) An iterative technique for Bayesian redshift estimates, which extracts the
full two-dimensional redshift and template probability function for each
galaxy. We demonstrate the performance of ZEBRA by applying it to a sample of
866 I_AB <= 22.5 COSMOS galaxies with available u*, B, V, g', r', i', z' and
K_s photometry and zCOSMOS spectroscopic redshifts in the range 0 < z < 1.3.
Adopting a 5-sigma-clipping that excludes less than 10 galaxies, both the
Maximum Likelihood and Bayesian estimates for this sample have an accuracy
dz/1+z better than 0.03. Similar accuracies are recovered using mock galaxies.
