Math and Architectures of Deep Learning

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 ...