16 Structured Probablistic Models for Deep Learning

A structured probablistic model is a way of describing a probablistic distribution, using a graph to descrie which random variable in the probablistic distribution interact with each other directly.

• 16.1 The Challenge of Unstructured Modeling
• 16.2 Using Graphs to Describe Model Structure
• 16.3 Sampling from Graphical Models
• 16.4 Advantages of Structured Modeling
• 16.5 Learning about Dependencies
• 16.6 Inference and Approximate Inference
• 16.7 The Deep Learning Approach to Structured Probabilistic Models