Graphical Markov models use graphs with nodes corresponding to random variables, and edges that encode conditional independence relationships between those variables. Directed graphical models (aka Bayesian networks) in particular have received considerable attention. This lecture will review basic concepts in graphical model theory such as Markov properties, equivalence, and connections with causal inference.