Structure of mixture models

Mixture Models in R

Victor Medina

Researcher at The University of Edinburgh

Description of mixture models

1. Which is the suitable probability distribution?
   • Get familiar with different probability distributions.
2. How many sub-populations should we consider?
   • Data scientist or statistical criteria.
3. What are the parameters and their estimations?
   • Awesome method called EM algorithm!

Example 1: Gender data set

Example 1: Gender dataset results

1. Which distribution? Bivariate Gaussian distribution
2. How many clusters? Two clusters
3. What are the estimates? Means, Standard deviations and proportions

Example 2: Handwritten digits

Example 2: Handwritten digits results

1. Which distribution? Bernoulli distribution
2. How many clusters? Two clusters
3. What are the estimates? The mean probability of being 1 for every dot and proportions

Example 3: Crime types

Example 3: Crime types results

1. Which distribution? Multivariate Poisson distribution
2. How many clusters? Six clusters
3. What are the estimates? Average number of crimes by type and proportions

Let's practice!

Mixture Models in R