  A nonparametric and locally adaptive Bayesian estimator is proposed for
estimating a binary regression. Flexibility is obtained by modeling the binary
regression as a mixture of probit regressions with the argument of each probit
regression having a thin plate spline prior with its own smoothing parameter
and with the mixture weights depending on the covariates. The estimator is
compared to a single spline estimator and to a recently proposed locally
adaptive estimator. The methodology is illustrated by applying it to both
simulated and real examples.
