| 135 | | * |
| | 135 | * "You've got your data, it's not random anymore.... You've run your experiment, your data certainly aren't random." |
| | 136 | * Partition is a collection of sets. |
| | 137 | * Axioms |
| | 138 | * Total probability - Sum of all events in a partition = 1 |
| | 139 | * Marginal probability - Sum of probability of event E intersected with all possible events in the partition. |
| | 140 | * Likelihood ratio x prior odds = posterior odds |
| | 141 | * Standard distributions |
| | 142 | * Discrete random variable |
| | 143 | * Pr(Y=y) = p(y) probability density function, must be 0 <= p(y) <= 1 & sum of all p(y) = 1 |
| | 144 | * Probability densities |
| | 145 | * Always >=0, sum of all area under curve = 1 |
| | 146 | * Binary distribution |
| | 147 | * Y={1,0} |
| | 148 | * Pr(Y=y|θ) = p(y|θ) = θ^y(1-θ)^(1-y) |
| | 149 | * Binomial distribution |
| | 150 | * Likelihood inference |
| | 151 | * Poisson distribution |
| | 152 | * Why use gamma vs beta? |
| | 153 | |
| | 154 | === Thursday AM1 === |
| | 155 | * Posterior is proportional to the likelihood times the prior (colloquial). |
| | 156 | * Estimation, hypothesis testing, prediction. |
| | 157 | * Beta distribution as a prior: |
| | 158 | * Mean of beta is a/(a+b) |
| | 159 | * Beta is flexible, to a degree |
| | 160 | * Uniform prior is tricky, even though its "uninformative", its not uniform on all scales. |
| | 161 | * Conjugate: posterior is the same form as the prior. |
| | 162 | * Theta^(y+a-1) * (1-Theta)^(N-y+b-1) |
| | 163 | * Posterior mean: (y+a)/(N+a+b) |
| | 164 | * Weighted estimator of sample mean and prior mean |
| | 165 | * As sample size increases sample mean weight increases |
| | 166 | * With fixed sample size increasing a+b (beta parameters) increases weight on prior. |
| | 167 | * Nonsymmetric vs asymmetric? |
| | 168 | * Averaging out over all the possible values theta could takeweighted by the posterior (Averaging out the uncertainty). |
| | 169 | * Hypothesis testing: |
| | 170 | * Ratio of probabilities of data given model 1 vs model 2 or null. |
| | 171 | * "Bayesian modelling can be very intoxicating." |
| | 172 | === Thursday AM2 === |