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In this chapter we introduce the Sparse Gaussian Process Regression (SGPR), which tries to solve this problem. The SGPR tries to approximate the N actual data points with M "inducing variables". With inducing variables the SGPR can make predicitions in O ( N M 2), which can make a big difference if M << N.