  We analyze the three-year WMAP temperature anisotropy data seeking to confirm
the power spectrum and likelihoods published by the WMAP team. We apply five
independent implementations of four algorithms to the power spectrum estimation
and two implementations to the parameter estimation. Our single most important
result is that we broadly confirm the WMAP power spectrum and analysis. Still,
we do find two small but potentially important discrepancies: On large angular
scales there is a small power excess in the WMAP spectrum (5-10% at l<~30)
primarily due to likelihood approximation issues between 13 <= l <~30. On small
angular scales there is a systematic difference between the V- and W-band
spectra (few percent at l>~300). Recently, the latter discrepancy was explained
by Huffenberger et al. (2006) in terms of over-subtraction of unresolved point
sources. As far as the low-l bias is concerned, most parameters are affected by
a few tenths of a sigma. The most important effect is seen in n_s. For the
combination of WMAP, Acbar and BOOMERanG, the significance of n_s =/ 1 drops
from ~2.7 sigma to ~2.3 sigma when correcting for this bias. We propose a few
simple improvements to the low-l WMAP likelihood code, and introduce two
important extensions to the Gibbs sampling method that allows for proper
sampling of the low signal-to-noise regime. Finally, we make the products from
the Gibbs sampling analysis publically available, thereby providing a fast and
simple route to the exact likelihood without the need of expensive matrix
inversions.
