Getting above the fray with lifted inference

Hi, I’m Jon. In my series of posts, I’ll be writing about how we can use the modern Bayesian toolkit to efficiently make decisions, solve problems, and formulate plans (the providence of AI), rather than restrict ourselves to approximating posteriors (the providence of statistics and much of machine learning).

Here’s a simple example of how AI can help out machine learning. What was the first graphical model you were exposed to? There’s a good chance it was Pearl’s famous “Sprinkler, Rain, Wet grass” graphical model[1]. Continue reading “Getting above the fray with lifted inference”