13 Fully Bayes model parameter estimation

This chapter covers

  • Fully Bayes parameter estimation for unsupervised modeling
  • Injecting prior belief into parameter estimation
  • Estimating Gaussian likelihood parameters with known or unknown mean and precision
  • Normal-gamma and Wishart distributions

Suppose we have a data set of interest: say, all images containing a cat. If we represent images as points in a high-dimensional feature space, our data set of interest forms a subspace of that feature space. Now we want to create an unsupervised model for our data set of interest. This means we want to identify a probability density function p() whose sample ...

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