  We derive various error exponents in the bee identification problem under two
different decoding rules. Under na\"ive decoding, which decodes each bee
independently of the others, we analyze a general discrete memoryless channel
and a relatively wide family of stochastic decoders. Upper and lower bounds to
the random coding error exponent are derived and proved to be equal at
relatively high coding rates. Then, we propose a lower bound on the error
exponent of the typical random code, which improves upon the random coding
exponent at low coding rates. We also derive a third bound, which is related to
expurgated codes, which turns out to be strictly higher than the other bounds,
also at relatively low rates. We show that the universal maximum mutual
information decoder is optimal with respect to the typical random code and the
expurgated code. Moving further, we derive error exponents under optimal
decoding, the relatively wide family of symmetric channels, and the maximum
likelihood decoder. We first propose a random coding lower bound, and then, an
improved bound which stems from an expurgation process. We show numerically
that our second bound strictly improves upon the random coding bound at an
intermediate range of coding rates, where a bound derived in a previous work no
longer holds.
