Worked solutions to all 342 exercises in Adnan Darwiche's Modeling and Reasoning with Bayesian Networks — propositional logic, probability calculus, building Bayesian networks, exact inference by variable elimination, factor elimination and conditioning, jointrees and graph decomposition, MPE and MAP, complexity, compilation, belief propagation, sampling, sensitivity analysis, and parameter/structure learning.
A student's guide to COMP9418 at UNSW: what the course covers (Bayesian networks, exact and approximate inference, HMMs, learning), the Darwiche textbook, the open-book exam format, and free notes for every topic.
bayesian networks from first principles — the chain rule, conditional probability tables, d-separation and its valve intuition, markov blankets, i-maps, naive bayes classifiers and gaussian networks.
exact inference in graphical models — factors, variable elimination, elimination orders and treewidth, the jointree (junction tree) algorithm with message passing, and mpe/map queries.
λambda — Learner-Adaptive, Marks-Bound Drilling Agent — is a study
tool that keeps a working image of your mind: what you hold, where you
stall, and which exam marks each gap is costing you. It probes before it
teaches, teaches only what you missed, and drills until the fix survives a
variant. The protocol is open source at
lambda-agent; the hosted study
lounge is being built at lambda.fatfort.com.
■ your mind, as λ understands it
■ what the Language Model knows that is relevant to you
■ everything else the Language Model knows
the probabilistic graphical models section — bayesian and markov networks, exact and approximate inference, sequence models, and learning from data. built from unsw's comp9418.
Here are the books that I have taken the time to create metadata and/or notes for.