  Minimum mean squared error (MMSE) estimators of signals from samples
corrupted by jitter (timing noise) and additive noise are nonlinear, even when
the signal prior and additive noise have normal distributions. This paper
develops a stochastic algorithm based on Gibbs sampling and slice sampling to
approximate the optimal MMSE estimator in this Bayesian formulation.
Simulations demonstrate that this nonlinear algorithm can improve significantly
upon the linear MMSE estimator, as well as the EM algorithm approximation to
the maximum likelihood (ML) estimator used in classical estimation. Effective
off-chip post-processing to mitigate jitter enables greater jitter to be
tolerated, potentially reducing on-chip ADC power consumption.
