On the Consistency of Bayesian Function Approximation Using Step Functions
Heng Lian · Neural Computation · 2007
We consider the problem of estimating a step function with an unknown number of jumps under noisy observations on a grid. Under mild assumptions, the Bayesian approach is shown to produce a consistent estimate, even when the underlying true function is not piecewise constant. A simple prior is constructed to illustrate our assumptions.