Will Crichton

Time to live-tweet the probabilistic graphical models course I'm taking. Prepare for a quarter of Hot Takes on how probabilistic programming languages are the future. cc @stephtwang

I absolutely despise almost all convention and notation in probability. Every time someone revisits the basics, I'm reminded how overloaded the "P" function is. You can take the probability of an outcome, an event, a random variable equal to a value (not an outcome), ...

... a joint distribution (comma separated), a joint distribution (\cap separated). You can write P(X) to represent the probability distribution over x. You can write P(X = x) to represent the probability of a single thing happening. You can write p(..), P(..), P[..].

My goal is to learn probability as a tool of thought. What is a general process for destructuring problems with incomplete information into programs/algorithms? I've always felt like these notational mishaps have been an impediment to me learning this process.

"Ok I don't know this piece of information... so is that an event? Err no a random variable. I know it's a categorical variable, so my prior should be drawn from a categorical distribu... hmm or multinomial... Dirichlet.......?"