with predicted mean f|u ≈ 𝑁(𝑘 xz (𝑘 zz + 𝜎 2 𝐼) -1 )u and the variance is (𝑘 xx ) -𝑘 xz (𝑘 zz + 𝜎 2 𝐼) -1 𝑘 zx , which simply says that the function values f at the training points can be approximately described by the inducing points u and this in turn reduces the size of the covariance matrix we need to invert from 𝑛 × 𝑛 to 𝑚 × 𝑚 where 𝑚 ≪ 𝑛.