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 stochastic algorithms based on Gibbs sampling and slice sampling to
approximate optimal MMSE estimators in this Bayesian formulation. Simulations
demonstrate that these nonlinear algorithms can improve significantly upon the
linear MMSE estimator. Effective off-chip post-processing to mitigate jitter
enables greater jitter to be tolerated, potentially reducing on-chip ADC power
consumption.