(niet zo goed) Schattingen van het gemiddelde en de sd

Gebruik opnieuw de gegevens gender en ga ervan uit dat de variabele Weight een Gauss-verdeling volgt. We kunnen dan het gemiddelde en de standaarddeviatie schatten met dezelfde procedure als hiervoor. Het idee is dat je ziet dat deze aanpak niet altijd werkt, omdat de data uit meer dan één verdeling kan bestaan.

Deze oefening maakt deel uit van de cursus

Mixture Models in R

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Probeer deze oefening eens door deze voorbeeldcode in te vullen.

# Estimation of the mean
___ <- gender %>% 
  pull(___) %>% 
  ___()
mean_estimate
# Estimation of the standard deviation
sd_estimate <- ___ %>% 
  pull(___) %>% 
  ___()
sd_estimate

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Deze oefening maakt deel uit van de cursus

Mixture Models in R

SkillTag.level.intermediateSkillTag.label

4.8+

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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.

Exercise 1: Introductie van modelgebaseerde clustering Exercise 2: Clustering-methoden Exercise 3: Verken geslachtsgegevens Exercise 4: Gaussiaanse verdeling Exercise 5: Steekproeven uit een Gaussische verdeling nemen Exercise 6: (niet zo goed) Schattingen van het gemiddelde en de sd

Huidige oefening

Exercise 7: Gaussian mixturemodellen (GMM)Exercise 8: Simuleer een mengsel van twee Gaussische verdelingen Exercise 9: Histogram van Gaussian Mixture plotten Exercise 10: Mix van drie Gaussiaanse verdelingen

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.

Exercise 1: Structure of mixture models Exercise 2: Which probability distribution?Exercise 3: Handwritten digits dataset Exercise 4: Parameters estimation Exercise 5: Estimation given the probabilities Exercise 6: Calculating the probabilities Exercise 7: EM algorithm Exercise 8: Expectation function Exercise 9: Maximization function Exercise 10: Apply the two steps Exercise 11: Plot the estimated clusters

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.

Exercise 1: Univariate Gaussian Mixture Models Exercise 2: Number of clusters Exercise 3: Number of parameters Exercise 4: Univariate Gaussian Mixture Models with flexmix Exercise 5: Univariate case with flexmix Exercise 6: Extracting Parameters for Univariate Case Exercise 7: Visualizing Univariate Gaussian Mixture Model Exercise 8: Compare the results Exercise 9: Bivariate Gaussian Mixture Models Exercise 10: Cross-term from covariance matrix Exercise 11: Parameters in the bivariate case Exercise 12: Bivariate Gaussian Mixture Models with flexmix Exercise 13: Fit the model with cross-terms Exercise 14: Get the components Exercise 15: Create the ellipses Exercise 16: Visualize the clusters

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.

Exercise 1: Bernoulli Mixture Models Exercise 2: Binary images Exercise 3: How many values?Exercise 4: Bernoulli Mixture Models with flexmix Exercise 5: Handwritten digits with `flexmix`Exercise 6: Poisson Mixture Models Exercise 7: Discover the lambda Exercise 8: Sample from Poisson distribution Exercise 9: Poisson Mixture Models with flexmix Exercise 10: Crimes data with `flexmix`