Writing
Part 5: Learning Parameters of a Bayesian Network
Learning parameters of a Bayesian Network enjoys a decomposition that it makes a much friendly endeavor than that of it's cousin, the Markov Network.
Part 4: Monte Carlo Methods
Monte Carlo methods answer the inference task with a set of samples, sampled approximately from the target distribution. In total, they provide a supremely general toolset. However, to use them requires a skill for managing complexities of distributional convergence and autocorrelation.
Part 3: Variational Inference
Variational Inference, a category of approximate inference algorithms, achieves efficiency by restricting inference to a computationally friendly set of distributions. Using tools from information theory, we may find the distribution that best approximates results of exact inference.
Part 2: Exact Inference
Given a Probabilistic Graphical Model, exact inference algorithms exploit factorization and caching to answer questions about the system it represents.
Part 1: An Introduction to Probabilistic Graphical Models
Probabilistic Graphical Models are born from a remarkable synthesis of probability theory and graph theory. They are among our most powerful tools for managing nature's baffling mixture of uncertainty and complexity.
Bias-Variance Trade-Off
The bias-variance trade-off is a rare insight into the challenge of generalization.
Information Theory and Entropy
Entropy and its related concepts quantify the otherwise abstract concept of information. A tour reveals its relationship to information, binary encodings and uncertainty. Most intuitively, we're left with a simple analogy to 2D areas.
Generalized Linear Models
A Generalized Linear Model, if viewed without knowledge of their motivation, can be a confusing tool. It's easier to understand if seen as a two knob generalization of linear regression.
The Fisher Information
The Fisher Information quantifies the information an observation carries for a parameter. The quantification becomes intuitive once we see it measuring a certain geometric quality.