Here’s an outline of what we discussed during Wednesday’s Blackboard Collaborate class session:
0-60min: We discussed HW9, specifically #2, as an example of a discrete probability distribution (and how we can use it to compute “cumulative probabilities”)
60-80min: We discussed HW7 #8, specifically how the various probabilities can be organized and displayed in a tree diagram (as an example of what is more generally called a probabilistic graphical model.
See the notes regarding probabilistic graphical models below.
80-100mins: We returned to binomial random variables and started looking at the binomial distribution formula. We will pick it up with that on Monday. See also the Khan Academy video on this:
Probabilistic Graphical Models:
Via the wikipedia entry for probabilistic graphical model: “A graphical model or probabilistic graphical model (PGM) or structured probabilistic model is a probabilistic model for which a graph expresses the conditional dependence structure between random variables. They are commonly used in probability theory, statistics—particularly Bayesian statistics—and machine learning.”
For instance, take a quick look at Ch 8 of a textbook titled Pattern Recogition and Machine Learning, which is about such graphical models. Note that early chapters in the at textbook cover probability theory, probability distributions and linear regression!
See also this brief intro to the subject:
