log-probabilities are used instead of probabilities: avoids numerical underflow

Overlapping subproblems

Dynamic programming solves overlapping subproblems by storing results of subproblems to avoid redundant calculations

Entropy H = -Σ p(x) log₂ p(x) measures average surprise in bits

Entropy H = -Σ p(x) log₂ p(x) quantifies uncertainty in a system

log-loss / cross-entropy loss penalizes: confident wrong predictions more heavily

Log-loss penalizes confident incorrect predictions more heavily

to use log-transform: when data is right-skewed or spans multiple orders of magnitude

Log-transform: Apply when data is right-skewed or spans multiple orders of magnitude

the do-calculus does: computes interventional probabilities from observational data

Do-calculus computes interventional probabilities from observational data

Kolmogorov complexity

Kolmogorov complexity is uncomputable