Novel parameter priors for Bayesian signal identification

The problem of eliciting priors on the parameter space of a signal hypothesis is considered in this paper, and two lesser-known approaches are emphasized. Each yields conservative priors appropriate for data-dominated Bayesian parameter inference. They are based, respectively, on the principles of (i) a posteriori transformation invariance, and (ii) a priori maximum entropy. Novel priors on a wide class of signal models are deduced. Their ability to regularize inference of the di erence frequency between closely spaced tones is considered, and they are compared with the Ockham Prior which was studied in previous work.