Question
Answer the following about computational Bayesian analysis. For 10 points each:
[10m] Stan is one language that is useful for drawing samples and checking convergence using these methods. These numerical methods are exemplified by the Metropolis-Hastings algorithm.
ANSWER: Monte Carlo methods [accept MCMC or Markov Chain Monte Carlo methods; prompt on MC]
[10h] Stan is named after this man, known for pioneering the Monte Carlo method. His work on non-Euclidean metrics built a foundation for cellular automata theory.
ANSWER: Stanislaw (“sta-ni-slav”) Ulam
[10e] Packages that use Stan often fit mixed models, which contain both fixed effects and effects described by this word. Monte Carlo methods rely on “sampling” described by this adjective.
ANSWER: random effects [accept random sampling]
<Jay Kim, Other Science>
Summary
| 2024 Penn Bowl Berkeley | 11/02/2024 | Y | 2 | 25.00 | 100% | 100% | 50% |
| 2024 Penn Bowl CWRU | 11/02/2024 | Y | 4 | 10.00 | 100% | 0% | 0% |
| 2024 Penn Bowl Chicago | 11/02/2024 | Y | 8 | 17.50 | 100% | 63% | 13% |
| 2024 Penn Bowl Florida | 10/26/2024 | Y | 2 | 15.00 | 100% | 50% | 0% |
| 2024 Penn Bowl Harvard | 10/26/2024 | Y | 4 | 17.50 | 100% | 75% | 0% |
| 2024 Penn Bowl Mainsite | 11/02/2024 | Y | 3 | 26.67 | 100% | 100% | 67% |
| 2024 Penn Bowl Texas | 11/02/2024 | Y | 2 | 15.00 | 100% | 50% | 0% |
| 2024 Penn Bowl UK | 10/26/2024 | Y | 5 | 18.00 | 100% | 60% | 20% |
| 2024 Penn Bowl UNC | 10/26/2024 | Y | 3 | 16.67 | 100% | 33% | 33% |
Data
| Berkeley B | Berkeley C | 10 | 0 | 10 | 20 |
| Stanford | Berkeley A | 10 | 10 | 10 | 30 |