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  3. Mixture Models in R
  • 1

    Introduction to Mixture Models

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    In this chapter, you will be introduced to fundamental concepts in model-based clustering and how this approach differs from other clustering techniques. You will learn the generating process of Gaussian Mixture Models as well as how to visualize the clusters.

  • 2

    Structure of Mixture Models and Parameters Estimation

    In this chapter, you will be introduced to the main structure of Mixture Models, how to address different data with this approach and how to estimate the parameters involved. To accomplish the estimation, you will learn an iterative method called Expectation-Maximization algorithm.

  • 3

    Mixture of Gaussians with `flexmix`

    This chapter shows how to fit Gaussian Mixture Models in 1 and 2 dimensions with `flexmix` package. The data used is formed by 10.000 observations of people with their weight, height, body mass index and informed gender.

  • 4

    Mixture Models Beyond Gaussians

    In this module, you will learn how Mixture Models extends to consider probability distributions different from the Gaussian and how these models are fitted with `flexmix`. The datasets used are handwritten digits images and the number of crimes in Chicago city. For the first dataset you will find clusters that summarize the handwritten digits and for the second dataset, you will find clusters of communities where is more or less dangerous to live in.

Initializing

Ubung

Mixture of three Gaussian distributions

What will change if we incorporate another distribution into our simulation? You will see that increasing the number of components will spread the mass density to include the extra distribution, but the logic still follows from the previous exercise.

Anweisungen

100 XP
  • Create assignments, which takes the values 0, 1 and 2 with a probability of 0.3, 0.4 and 0.3, respectively.
  • The data frame mixture samples from a Gaussian with a mean of 5 and sd of 2, when assignments is 1. If assignments is 2, the mean is 10 and sd is 1. Otherwise, is a standard normal distribution.
  • Plot the histogram with 50 bins.