Introduction to Monte Carlo Methods

Lecture, three hours; discussion, one hour. Requisites: courses 100B (or Mathematics 170S), 102A. Introduction to Markov chain Monte Carlo (MCMC) algorithms for scientific computing. Generation of random numbers from specific distribution. Rejection sampling and importance sampling and their roles in MCMC. Markov chain theory and convergence properties. Metropolis and Gibbs sampling algorithms. Extensions as simulated tempering. Theoretical understanding of methods and their implementation in concrete computational problems. P/NP or letter grading.

Review Summary

Clarity
8.3 / 10
Organization
8.3 / 10
Time
10-15 hrs/week
Overall
8.3 / 10

Reviews

    Quarter Taken: Fall 2023 In-Person
    Grade: A

    Great professor! Very clear lecture slides! Overall, I learned a lot from the class.

    Quarter Taken: Fall 2023 In-Person
    Grade: A+

    Fantastic professor that every Stats major should take if given the chance!

Course

Instructor
Miles Chen
Previously taught
23F 21F

Grading Information

  • No group projects

  • Attendance not required

  • 2 midterms

  • Finals week final

  • 0% recommend the textbook

Previous Grades

Grade distributions not available.