= Peter Bartlett
{c}
<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.https://statistics.berkeley.edu/people/peter-bartlett[Institutional research statement]{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.<Introduction to Time Series Analysis, Lecture 20 by Peter Bartlett>[Slides 2–9, periodogram asymptotics and Gaussian white-noise calculation]{ref}
\Image[https://statistics.berkeley.edu/sites/default/files/styles/crop_person/public/faculty/Peter-Bartlett2.jpg?h=cdfd68d0&itok=hp_wnsMa]
{title=Peter Bartlett}
{description=Portrait identified by the UC Berkeley Department of Statistics profile.}
{source=https://statistics.berkeley.edu/people/peter-bartlett}
{width=220}
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