  Peer prediction incentive mechanisms for crowdsourcing are generally limited
to eliciting samples from categorical distributions. Prior work on extending
peer prediction to arbitrary distributions has largely relied on assumptions on
the structures of the distributions or known properties of the data providers.
We introduce a novel class of incentive mechanisms that extend peer prediction
mechanisms to arbitrary distributions by replacing the notion of an exact match
with a concept of neighborhood matching. We present conditions on the belief
updates of the data providers that guarantee incentive-compatibility for
rational data providers, and admit a broad class of possible reasonable
updates.
