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 \ell \lesssim 30 ) primarily due to likelihood approximation issues between 13 \leq \ell \lesssim 30 .
On small angular scales there is a systematic difference between the V- and W-band spectra ( few percent at \ell \gtrsim 300 ) .
Recently , the latter discrepancy was explained by Huffenberger et al .
( 17 ) in terms of over-subtraction of unresolved point sources .
As far as the low- \ell bias is concerned , most parameters are affected by a few tenths of a sigma .
The most important effect is seen in n _ { \textrm { s } } .
For the combination of WMAP , Acbar and BOOMERanG , the significance of n _ { \textrm { s } } \neq 1 drops from \sim 2.7 \sigma to \sim 2.3 \sigma when correcting for this bias .
We propose a few simple improvements to the low- \ell 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 .