To run this interactive demo, enter one or more values in the evidence section and click calculate.

A mixture model can be represented using a Bayesian network as shown in the network diagram below. The model shown here was learnt using the continuous variables {Sepal Length, Sepal Width, Petal Length and Petal Width} from the well known Iris data set. The Bayes Server network file for this model can be found here.

This interactive demo charts the joint distribution of Petal Length and Petal Width, i.e. P(Petal length, Petal width), calculated using the Bayes Server API before evidence (Gray) and given evidence (Red). It also calculates the log likelihood of evidence entered and the posterior Cluster probabilities.

This is an example of multivariate modelling. In this simple example we are updating our beliefs about the joint distribution of Petal length and Petal width given what we know (evidence) about other variables.

The discrete cluster variable (mixture component) allows us to model more complex distributions by combining simpler distributions each with different importance (weight).