| | 119 | |
| | 120 | === SISG 22 - Bayesian Stats === |
| | 121 | [http://www.stat.washington.edu/people/pdhoff/ Peter Hoff] & Jon Wakefield |
| | 122 | |
| | 123 | [https://catalyst.uw.edu/workspace/pdhoff/50924 Lecture notes and materials] |
| | 124 | === Wednesday PM1 === |
| | 125 | * Probability as belief or information, quantifying uncertainty. |
| | 126 | * Information / uncertainty, are they 1-to-1? There is a relationship between information and proability. |
| | 127 | * "There's good induction, and there's bad induction." |
| | 128 | * "We'll talk about, at the end, what to do if you don't have any beliefs." |
| | 129 | * Y-axis of beta distribution is a probability density, a dimensionless quantity. |
| | 130 | * '''Posterior expectation is the weighted average of the data mean + prior mean. Lots of data makes posterior expectation closer to data mean, less data makes posterior closer to prior mean. o_O''' |
| | 131 | * Probability of rare events/Predictive models |
| | 132 | * Prior distribution: Idealistic vs realistic, capture the gross features about the priors... |
| | 133 | * ML tends to overfit to the data when you have a large parameter space relative to the sample size... |
| | 134 | === Wednesday PM2 === |
| | 135 | * |
| | 136 | |