7.4 How to choose K

One of the main concerns with finite mixture models is how to decide on the number of components. A rule of thumb is to begin with a relatively small number of components and then increase it to improve the model-fit evaluation. As we already know from Chapter 5, model fit can be evaluated using posterior-predictive checks, metrics such as the ELPD, and the expertise of the modeler(s).

Let us compare the model for K = {2,3,4,5}. To do this, we are going to fit the model four times and then save the data and model objects for later use:

Code 7.4

Ks = [2, 3, 4, 5] 
 
models = [] 
idatas = [] 
for k in Ks: 
    with pm.Model() as model: 
        p = pm.Dirichlet('p', a=np.ones(k)) 
        means = pm.Normal('means', 
               ...