Approximate computational approaches for Bayesian sensor placement in high dimensions.

•Approximate Bayesian sensor placement in high dimensions is proposed.•Lower bound on mutual information obtained via data processing inequality.•Canonical correlation directions are used to project high dimensional data.•Bayesian optimization is used to maximize the proposed lower bound.•Sensor placement methodology is applied to a chemical release problem.