Week 11: Baby Steps Towards Gaussian Processes

DSAN 5300: Statistical Learning
Spring 2026, Georgetown University

Author

Affiliation

Jeff Jacobs

jj1088@georgetown.edu

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Schedule

Today’s Planned Schedule:

	Start	End	Topic
Lecture	6:30pm	7:30pm	Past Final Projects!
	7:30pm	8:00pm	Bayesian Linear Regression →
Break!	8:00pm	8:10pm
	8:10pm	9:00pm	Adventures in Covariance →

source("../dsan-globals/_globals.r")
set.seed(5300)

\[ \DeclareMathOperator*{\argmax}{argmax} \DeclareMathOperator*{\argmin}{argmin} \newcommand{\bigexp}[1]{\exp\mkern-4mu\left[ #1 \right]} \newcommand{\bigexpect}[1]{\mathbb{E}\mkern-4mu \left[ #1 \right]} \newcommand{\definedas}{\overset{\small\text{def}}{=}} \newcommand{\definedalign}{\overset{\phantom{\text{defn}}}{=}} \newcommand{\eqeventual}{\overset{\text{eventually}}{=}} \newcommand{\Err}{\text{Err}} \newcommand{\expect}[1]{\mathbb{E}[#1]} \newcommand{\expectsq}[1]{\mathbb{E}^2[#1]} \newcommand{\fw}[1]{\texttt{#1}} \newcommand{\given}{\mid} \newcommand{\green}[1]{\color{green}{#1}} \newcommand{\heads}{\outcome{heads}} \newcommand{\iid}{\overset{\text{\small{iid}}}{\sim}} \newcommand{\lik}{\mathcal{L}} \newcommand{\loglik}{\ell} \DeclareMathOperator*{\maximize}{maximize} \DeclareMathOperator*{\minimize}{minimize} \newcommand{\mle}{\textsf{ML}} \newcommand{\nimplies}{\;\not\!\!\!\!\implies} \newcommand{\orange}[1]{\color{orange}{#1}} \newcommand{\outcome}[1]{\textsf{#1}} \newcommand{\param}[1]{{\color{purple} #1}} \newcommand{\pgsamplespace}{\{\green{1},\green{2},\green{3},\purp{4},\purp{5},\purp{6}\}} \newcommand{\pedge}[2]{\require{enclose}\enclose{circle}{~{#1}~} \rightarrow \; \enclose{circle}{\kern.01em {#2}~\kern.01em}} \newcommand{\pnode}[1]{\require{enclose}\enclose{circle}{\kern.1em {#1} \kern.1em}} \newcommand{\ponode}[1]{\require{enclose}\enclose{box}[background=lightgray]{{#1}}} \newcommand{\pnodesp}[1]{\require{enclose}\enclose{circle}{~{#1}~}} \newcommand{\purp}[1]{\color{purple}{#1}} \newcommand{\sign}{\text{Sign}} \newcommand{\spacecap}{\; \cap \;} \newcommand{\spacewedge}{\; \wedge \;} \newcommand{\tails}{\outcome{tails}} \newcommand{\Var}[1]{\text{Var}[#1]} \newcommand{\bigVar}[1]{\text{Var}\mkern-4mu \left[ #1 \right]} \]

Bayesian Linear Regression

Data-Generating Processes (DGPs)

• You saw this in DSAN 5100!
• «\(X_1, \ldots, X_n\) drawn i.i.d. Normal, mean \(\mu\) variance \(\sigma^2\)» characterizes DGP of \((X_1, \ldots, X_n)\)

And Regression?

Linear Regression as a PGM (Source)

Learning Parameters Sequentially

From Gelman et al. (1995)

Adventures in Covariance

Why Should We Care About Covariance?

From McElreath (2020)

References

Gelman, A., J. B. Carlin, H. S. Stern, and D. B. Rubin. 1995. Bayesian Data Analysis. Chapman and Hall.

McElreath, Richard. 2020. Statistical Rethinking: A Bayesian Course with Examples in R and STAN. CRC Press. https://books.google.com?id=FuLWDwAAQBAJ.