We have previously looked at a dataset with fatal airplane accidents. First, we created two different models for the count of fatal accidents:
Our previous look at exercise 2.11.13 continues in exercise 6.7.2 with posterior predictive…
we are task to make a Multinomial model for categorical data, in particular the different color M&Ms.
```{stan, output.var="model"}data { int<lower=1> N; // observations int<lower=1> K; // categories int<lower=0> y[N,K]; // observations}…
Following the structure of This, we will construct a model for part c) for 2.11.13 in…
To give us another close look at modeling and computing using RStan, here’s exercise 2.11.13.
We start out with a table of fatal airplane accidents and deaths per year over a 10-year period. The death rate is number of passenger deaths per 1e8…
In our last installment, we defined exponential families and gave examples from a range of familiar distributions. Now I would like to spend some time looking at a few reasons why we should care about these families in the first place.
We will be digging far deeper into this as the semester goes on, but having already seen a first Bayesian…
There are a LOT of different p floating around anything probability related, and it’s not always clear how they fit…
