STATS 102C
Introduction to Monte Carlo Methods
Lecture and discussion. The registrar has published a final exam time.
Fall 2026: 4 lectures and 8 discussions. Nothing here is open. The oldest of these readings was 3 hours ago.
Grades
3.36
Average grade awarded in STATS 102C. Not a student rating: this is what the grades were.
In the A range
59%
of the 1,303 letter grades
D, F or W
4.1%
of every letter grade awarded, plus withdrawals
Taken pass/no pass
<1%
3 of 3 students passed
The registrar's own records: 1,303 grades over 9 terms, Fall 2021 to Fall 2025. Every instructor is pooled here.
What is inside that record
4 of those terms are summer sessions, and summer sessions are counted here.
The table below splits them, and term averages across those 9 terms ran 2.94 to 3.81.
Historical grades for CHEN, MILES SATORI, across recorded terms. The course summary above includes all professors.
- A+ 32 · 14.3%
- A 100 · 44.8%
- A- 31 · 13.9%
- B+ 19 · 8.5%
- B 22 · 9.9%
- B- 6 · 2.7%
- C+ 8 · 3.6%
- C 2 · 0.9%
- D 2 · 0.9%
- F 1 · 0.4%
- P 1 of 224
Percentages are of the 223 letter grades. Grey bars are non-letter outcomes: passes (P).
How the non-letter outcomes are counted
They are excluded from the GPA entirely rather than scored, because counting a P as a 4.0 would be a fabrication.
By instructor
| Instructor | Predicted GPA | Raw | n | Terms | A range | Workload | Fall 2026 |
|---|---|---|---|---|---|---|---|
| WU, GUANI reviews ↗ | 3.00 | 2.99 | 620 | 6 | 38% | heavy | Closed by the department |
| CHEN, MILES SATORI reviews ↗ | 3.62 | 3.64 | 223 | 3 | 73% | average | Closed by the department |
| TSIANG, MICHAEL reviews ↗ | 3.74 | 3.78 | 187 | 2 | 84% | light | Closed by the department |
| ONYAMBU, SAMUEL ONYANCHA reviews ↗ | 3.65 | 3.68 | 129 | 1 | 74% | - | not teaching it |
| ZHOU, QING reviews ↗ | 3.55 | 3.59 | 77 | 2 | 69% | average | not teaching it |
| WU, YINGNIAN reviews ↗ | 3.70 | 3.78 | 67 | 1 | 88% | light | not teaching it |
Why Predicted is not the Raw average
“Predicted” is shrunk toward the course average, which is itself shrunk toward the department, so an instructor with a handful of students sits near the course mean rather than topping the list.
Grade records describe outcomes. For what a course is like to take, read student reviews written on dibs below or follow the separate external review link.
Workload and assignments
Work types reported in BruinWalk reviews of this course, across recorded instructors and terms. They may differ from this term's syllabus.
dibs has not read this course’s reviews, so it cannot say what kind of work it sets. That is dibs not having looked. It is not a course without papers or exams, and the reviews linked below are the answer until dibs catches up.
What comes first
The chain dibs can see is 6 courses long before STATS 102C. This is a floor, not a deadline: dibs has not read a class page for MATH 170A, MATH 3B, and STATS 102A, so the real chain can only be longer than this, never shorter.
Counted in courses rather than quarters: dibs does not know which of them a department lets you take together.
In Falls
Ran in 5 of the last 5 Falls.
From the terms dibs holds, which start in Fall 2021 and exclude summer. A department can run a course in a quarter it never has before.
From the registrar's catalog
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.
Sections in Fall 2026
You enroll in the discussion, not the lecture, so a discussion's status is the one that decides whether you get in. A lecture can read open while every discussion under it is full.
Seat counts as of : the oldest of these 12 readings. Seat readings are snapshots, not live availability. Their timestamps show when dibs last checked.
-
Lec 1 MW 5:00pm-6:15pmClosed by the department3.36predicted, all instructors · 1,303 grades
85 enrolled no history
2 discussions, none open. There is no waitlist either, so the only way in is somebody dropping the lecture.
This limit is on the class, so it applies to all 2 discussions below.
-
Lec 2 MWF 8:00am-8:50amClosed by the department3.62predicted, this instructor · 223 grades
83 enrolled no history
2 discussions, none open. There is no waitlist either, so the only way in is somebody dropping the lecture.
This limit is on the class, so it applies to all 2 discussions below.
-
Lec 3 TR 5:00pm-6:15pmClosed by the department3.74predicted, this instructor · 187 grades
83 enrolled no history
2 discussions, none open. There is no waitlist either, so the only way in is somebody dropping the lecture.
This limit is on the class, so it applies to all 2 discussions below.
-
Lec 4 TR 12:30pm-1:45pmClosed by the department3.00predicted, this instructor · 620 grades
77 enrolled no history
2 discussions, none open. There is no waitlist either, so the only way in is somebody dropping the lecture.
This limit is on the class, so it applies to all 2 discussions below.
Final enrollment, averaged across each term's sections: between 55 and 80 a term over 5 terms, summer excluded, most recently 55 in Fall 2025. That is the shape across years, not this term, which is above.
From the people who took it
Student reviews
What taking STATS 102C was like: the work, the teaching, and what students wish they’d known.
Your experience could be the first.
No student reviews are available to read on dibs for this course yet. Took it? Tell the next person what helped, what was hard, and how you spent your time.
Looking for more perspectives?
External reviews are separate and do not count toward the dibs rating.
Seats over time in Fall 2026
How each lecture has filled since dibs first saw it. This course runs 4 lectures; the legend under the chart picks one.
A dot is a reading where something moved: the seat count, the status, or the waitlist.
What a flat run means, and where a line starts
Flat between dots means it did not drift, it held and then jumped, and a line starts when dibs first saw that lecture rather than reaching back before it existed. A lecture that has never moved draws a flat line across the whole window, and one with two or three readings draws a line joining them, which is too few to read a trend from.
Lec 1 · Handcock, M.S. · 85 enrolled
Nothing read since 1 Oct, 3 hours ago. Over about 1 month before that, filled by 5.
Lec 2 · Chen, M.S. · 83 enrolled
Nothing read since 1 Oct, 3 hours ago. Over about 1 month before that, filled by 3.
Lec 3 · Tsiang, M. · 83 enrolled
Nothing read since 1 Oct, 3 hours ago. Over about 1 month before that, filled by 3.
Nothing read since 1 Oct, 3 hours ago. Over about 1 month before that, freed 3 seats.
How readings and alerts work
A reading and an hourly record are different things, and most charts here hold more of the second missing than present. dibs has taken readings of a section since long before it began keeping an hour-by-hour log of when it looked, so the empty stretches are dibs saying it was not keeping that log yet, not that it looked and found nothing. That is the age of the record and not a fault.
Seat counts show the last successful read, not live availability. Refreshes can be delayed by request limits or upstream interruptions. Watched sections receive priority, but an hourly update for every section is not guaranteed.
The registrar can change a seat count between our checks. Alerts depend on dibs detecting an opening and completing delivery; a quiet watch does not prove that no seat opened. Check the registrar before enrolling.