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Aditya Guntuboyina (Instructor)

aditya@stat.berkeley.edu

Office Hours (Evans 443):

  • Monday and Friday 2-3 pm

Reece Huff (GSI)

rdhuff@berkeley.edu

Office Hours (Evans 444):

  • Mondays 10-11 am

  • Tuesdays 2-3 pm

  • Wednesdays 11 am - 12 pm

Syllabus

Basic information about the course can be found in the syllabus.

Topics

Here is a tentative list of topics.

Schedule

Week 1
Jan 21Lecture 1Introduction
PDF 1LectureOne238Spring2026.pdf
Jan 23Lecture 2Simple Examples
PDF 2LectureTwo238Spring2026.pdf
Week 2
Jan 26Lecture 3Normal Mean Example
PDF 3LectureThree238Spring2026.pdf
Jan 27Lab 1Bayes Rule, LOTP, and Model Selection
PDF Lab 1lab01.pdf
Jan 28Lecture 4Likelihood Principle
PDF 4LectureFour238Spring2026.pdf
Jan 30Lecture 5Bayesian Testing
PDF 5LectureFive238Spring2026.pdf
Week 3
Feb 02Lecture 6Interpretation and Rules of Probability
PDF 6LectureSix238Spring2026.pdf
Feb 03Lab 2Uninformative Priors
Lab 2 CodeFrequentist vs. Bayesian Inference
PDF Lab 2lab02.pdf
Feb 04Lecture 7Recap of the derivation of the rules of probability
Lecture 7 CodeKidney Cancer Data Analysis
PDF 7LectureSeven238Spring2026.pdf
Dataset 1Kidney Cancer Dataset
Feb 06Lecture 8Beta-Binomial Inference
PDF 8LectureEight238Spring2026.pdf
Week 4
Feb 09Lecture 9 CodeKidney Cancer Data Analysis with Bayesian Methods
Dataset 1Kidney Cancer Dataset
Feb 10Lab 3 CodeProbabilistic Programming via PyMC
Feb 11Lecture 10Additional comments on kidney cancer data analysis, and normal likelihoods
Lecture 10 CodeMore on Kidney Cancer Data Analysis, and the baseball dataset
PDF 10LectureTen238Spring2026.pdf
Feb 13Lecture 11Normal likelihoods, and the James-Stein estimator
Lecture 11 CodeNormal-Normal Bayesian model applied to the baseball dataset
PDF 11LectureEleven238Spring2026.pdf
Week 5
Feb 17Lab 4 CodePosterior Predictive Distribution
Feb 18Lecture 12Dirichlet-Multinomial Inference
Lecture 12 CodeDirichlet-Multinomial Inference
PDF 12LectureTwelve238Spring2026.pdf
Feb 20Lecture 13Bayesian Bootstrap
Lecture 13 CodeBayesian Bootstrap
PDF 13LectureThirteen238Spring2026.pdf
Week 6
Feb 23Lecture 14Simple text analysis example
Lecture 14 CodeSimple text analysis example
PDF 14LectureFourteen238Spring2026.pdf
Feb 24Lab 5 CodeBayesian Bootstrap
Feb 25Lecture 15Linear Regression
PDF 15LectureFifteen238Spring2026.pdf
Week 7
Mar 02Lecture 16Linear regression -- frequentist and Bayesian inference
Lecture 16 CodeLinear Regression
PDF 16LectureSixteen238Spring2026.pdf
Mar 03Lab 6 CodeLinear Regression Details
Dataset 2GDP Dataset
Mar 04Lecture 17Logistic Regression
Lecture 17 CodeLogistic Regression
PDF 17LectureSeventeen238Spring2026.pdf
Mar 06Lecture 18 CodeHigh-dimensional linear regression
Week 8
Mar 09Lecture 19High-dimensional linear regression -- Bayesian inference
Lecture 19 CodeHigh-dimensional linear regression
PDF 19LectureNineteen238Spring2026.pdf
Mar 10Lab 7 CodePoisson Regression
Dataset 3MROZ Dataset
Mar 11Lecture 20Bayesian inference for high-dimensional linear regression (continued)
Lecture 20 CodeBayesian inference for high-dimensional linear regression (continued)
PDF 20LectureTwenty238Spring2026.pdf
Mar 13Lecture 21Introduction to Gaussian Process Regression
Lecture 21 CodeSimulating Brownian Motion
PDF 21LectureTwentyOne238Spring2026.pdf
Week 9
Mar 16Lecture 22Interpolation and Integration using Gaussian Processes
Lecture 22 CodeInterpolation with Gaussian Processes
PDF 22LectureTwentyTwo238Spring2026.pdf
Mar 17Lab 8 CodeBayesian Regularization
Mar 18Lecture 23Regression using Gaussian Processes
Lecture 23 CodeGaussian Process Regression
PDF 23LectureTwentyThree238Spring2026.pdf
Week 10
Mar 30Lecture 24MCMC: The Metropolis Algorithm
Lecture 24 CodeMCMC via the Metropolis Algorithm
PDF 24LectureTwentyFour238Spring2026.pdf
Mar 31Lab 9 CodeGP Regression with the squared exponential kernel
Apr 01Lecture 25Markov chains, detailed balance, Metropolis-Hastings and Gibbs
PDF 25LectureTwentyFive238Spring2026.pdf
Apr 03Lecture 26The Gibbs Sampler
Lecture 26 CodeGibbs sampler -- simple examples
PDF 26LectureTwentySix238Spring2026.pdf
Week 11
Apr 06Lecture 27More on the Gibbs Sampler
Lecture 27 CodeMore on the Gibbs Sampler
PDF 27LectureTwentySeven238Spring2026.pdf
Apr 07Lab 10 CodeMetropolis Hastings applied to Poisson Regression
Apr 08Lecture 28Gibbs Sampler for Mixture Models
Lecture 28 CodeGibbs Sampler and EM for Mixture Models
PDF 28LectureTwentyEight238Spring2026.pdf
Apr 10Lecture 29More on the Gibbs sampler for Mixture Models
Lecture 29 CodeGibbs Sampler for Mixture Models -- exoplanet data
PDF 29LectureTwentyNine238Spring2026.pdf
Dataset 4The Exoplanet Dataset
Week 12
Apr 13Lecture 30Gibbs Sampler for Normal Mixtures
Lecture 30 CodeGibbs Sampler for Normal Mixtures
PDF 30LectureThirty238Spring2026.pdf
Apr 14Lab 11 CodeGibbs Sampler applied to Mixture of Regressions
Apr 15Lecture 31Metropolis Adjusted Langevin Algorithm (MALA)
Lecture 31 CodeRevisiting Random Walk Metropolis
PDF 31LectureThirtyOne238Spring2026.pdf
Apr 17Lecture 32RWM, MALA and HMC
Lecture 32 CodeMALA
PDF 32LectureThirtyTwo238Spring2026.pdf
Week 13
Apr 20Lecture 33Hamiltonian Monte Carlo
PDF 33LectureThirtyThree238Spring2026.pdf
Apr 21Lab 12 CodeRWM, MALA and HMC for the standard Gaussian
Apr 22Lecture 34Hamiltonian Dynamics and Leapfrog Discretization
PDF 34LectureThirtyFour238Spring2026.pdf
Apr 24Lecture 35Hamiltonian Monte Carlo -- algorithm and detailed balance
Lecture 35 CodeHamiltonian Monte Carlo applied to Bayesian Neural Networks
PDF 35LectureThirtyFive238Spring2026.pdf
Week 14
Apr 27Lecture 36Variational Inference and application to Logistic Regression
Lecture 36 CodeVariational Bayesian Logistic Regression
PDF 36LectureThirtySix238Spring2026.pdf
Apr 28Lab 13 CodeBayesian Neural Networks using HMC
Apr 29Lecture 37EM and Coordinatewise Variational Inference
PDF 37LectureThirtySeven238Spring2026.pdf
May 1Lecture 38Variational AutoEncoders
Lecture 38 CodeVariational AutoEncoders
PDF 38LectureThirtyEight238Spring2026.pdf
Stat 238
Staff