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
Enrollment Progress
Section List
LEC 3
ClosedMWF 8am-8:50am
Kinsey Science Teaching Pavilion 1240B
Reviews
Great professor! Very clear lecture slides! Overall, I learned a lot from the class.
Fantastic professor that every Stats major should take if given the chance!
Displaying all 2 reviews
Course
Grading Information
-
No group projects
-
Attendance not required
-
2 midterms
-
Finals week final
-
0% recommend the textbook
Previous Grades
Grade distributions not available.