In conventional interference cancellation (IC)-based turbo receivers, extrinsic information (EXT) from the soft-input–soft-output (SISO) decoders is used to compute the statistical mean (SM) of the interfering signals. In this paper, we present a class of Bayesian minimum mean-square-error (MMSE) turbo receivers for coded multiple-input–multiple-output (MIMO) systems. Instead of using the EXT to estimate the prior SM, we use it in the posterior Bayesian MMSE estimation of the interfering signals. The estimation accuracy is, therefore, improved, leading to better bit-error-rate (BER) performance from our proposed Bayesian receivers. For the cases that we have studied, the proposed receiver with two iterations has a lower BER than the conventional one with five iterations, thus significantly reducing the receiver processing delay and the implementation complexity. Its superior performance is also demonstrated by the higher detector output mutual information and fewer iterations to achieve convergence, which is obtained from the extrinsic information transfer (EXIT) chart analysis.