 Peter Bartlett

Peter Bartlett studies the foundations of machine learning, statistical learning theory and reinforcement learning. His Berkeley research description emphasizes the joint demands of computational efficiency and statistical performance, including prediction with complex outputs and sequential decisions.^[ref]

His Introduction to Time Series Analysis, Lecture 20 by Peter Bartlett gives a concrete lesson about estimation. The raw periodogram can have an approximately correct expected value while its variance remains substantial as the sample grows. For Gaussian white noise, an interior-frequency periodogram ordinate is distributed as a scaled chi-squared distribution with two degrees of freedom. Spectral smoothing is therefore doing statistical work, rather than merely making a graph look smoother.^[ref]