  This article is concerned with decentralized sequential testing of multiple
hypotheses. In a sensor network system with limited local memory, raw
observations are observed at the local sensors, and quantized into binary
sensor messages that are sent to a fusion center, which makes a final decision.
It is assumed that the raw sensor observations are distributed according to a
set of M>=2 specified distributions, and the fusion center has to utilize
quantized sensor messages to decide which one is the true distribution.
Asymptotically Bayes tests are offered for decentralized multihypothesis
sequential detection by combining three existing methodologies together: tandem
quantizers, unambiguous likelihood quantizers, and randomized quantizers.
