Packet O: Bonus 9
Examples of these algorithms include Gibbs sampling and the Metropolis–Hastings algorithm. For 10 points each:
[10m] Name this class of sampling methods typically referred to by a four-letter acronym. These methods aim to sample from the stationary distribution of a memoryless stochastic process.
ANSWER: Markov chain Monte Carlo [or MCMC; prompt on Markov chain or Markov processes; prompt on Monte Carlo methods]
[10e] This statement for MCMC models guarantees convergence to the target distribution. More generically, this statement states that the rescaled distribution of sample means converges to a normal distribution.
ANSWER: central limit theorem [or CLT]
[10h] Charlie Geyer critiqued this procedure as unnecessary since it appeals to a “central limit almost-but-not-quite theorem for almost-but-not-quite stationary processes.” This procedure involves initially running the Markov chain for n steps, and then throwing away the results.
ANSWER: burn-in
<GC, Other Science> | Packet O - Editors 1
| Heard | PPB | E % | M % | H % |
|---|---|---|---|---|
| 13 | 14.62 | 92% | 39% | 15% |
Conversion
| Team | Opponent | Part 1 | Part 2 | Part 3 | Total | Parts |
|---|---|---|---|---|---|---|
| Ohio State A | Michigan A | 0 | 10 | 0 | 10 | E |
Summary
| Tournament | Edition | Match | Heard | PPB | E % | M % | H % |
|---|---|---|---|---|---|---|---|
| Canada | Main Site | ✓ | 2 | 10.00 | 100% | 0% | 0% |
| Florida | Main Site | ✓ | 2 | 5.00 | 50% | 0% | 0% |
| Great Lakes | Main Site | ✓ | 1 | 10.00 | 100% | 0% | 0% |
| Lower Mid-Atlantic | Main Site | ✓ | 1 | 10.00 | 100% | 0% | 0% |
| Midwest | Main Site | ✓ | 2 | 25.00 | 100% | 100% | 50% |
| Northeast | Main Site | ✓ | 1 | 20.00 | 100% | 100% | 0% |
| Overflow | Main Site | ✓ | 1 | 10.00 | 100% | 0% | 0% |
| UK | Main Site | ✓ | 2 | 15.00 | 100% | 50% | 0% |
| Upper Mid-Atlantic | Main Site | ✓ | 1 | 30.00 | 100% | 100% | 100% |