  A lower bound on the minimum error probability for multihypothesis testing is
established. The bound, which is expressed in terms of the cumulative
distribution function of the tilted posterior hypothesis distribution given the
observation with tilting parameter theta larger than or equal to 1, generalizes
an earlier bound due the Poor and Verdu (1995). A sufficient condition is
established under which the new bound (minus a multiplicative factor) provides
the exact error probability in the limit of theta going to infinity. Examples
illustrating the new bound are also provided.
  The application of this generalized Poor-Verdu bound to the channel
reliability function is next carried out, resulting in two information-spectrum
upper bounds. It is observed that, for a class of channels including the
finite-input memoryless Gaussian channel, one of the bounds is tight and gives
a multi-letter asymptotic expression for the reliability function, albeit its
determination or calculation in single-letter form remains an open challenging
problem. Numerical examples regarding the other bound are finally presented.
