A novel estimator for mutual information is proposed. The estimator is useful for the (asymmetric) scenario where only a few samples for one random variable are available, but for each sample, the conditional distribution of the other random variable can be accurately characterized. Such asymmetry is common in neuroscience where it is often necessary to repeat the same stimulus many times to obtain a stable response.