statisticalrethinkin..

126 4. LINEAR MODELS4.7.1. e1. In the model definition below, which line is the likelihood?y i ∼ Normal(µ, σ)µ ∼ Normal(0, 10)σ ∼ Uniform(0, 10)4.7.2. e2. In the model definition just above, how many parameters are in the posterior distribution?4.7.3. e3. Using the model definition above, write down Bayes’ theorem. (See page 89 for an example.)4.7.4. e4. In the model definition below, which line is the linear model?y i ∼ Normal(µ, σ)µ i = α + βx iα ∼ Normal(0, 10)β ∼ Normal(0, 1)σ ∼ Uniform(0, 10)4.7.5. e5. In the model definition just above, how many parameters are in the posterior distribution?Medium.4.7.6. m1. For the model definition below, simulate observed heights from the prior (not the posterior).See pages 88–89 for an example.y i ∼ Normal(µ, σ)µ ∼ Normal(0, 10)σ ∼ Uniform(0, 10)4.7.7. m2. Translate the model just above into a map formula.4.7.8. m3. Translate the map model formula below into a mathematical model definition.flist