Top-k vs top-p sampling: top-k fixes candidate count, top-p fixes cumulative probability mass

importance sampling does: reweights samples from proposal to estimate target expectation

Why can't we always use the same samples to figure out what's happening in a complex system?

Markov chain Monte Carlo

MCMC samples from complex posterior distributions

Boosting (machine learning)

Boosting reduces bias in ML models

GraphSAGE does: samples and aggregates a fixed-size neighborhood

GraphSAGE samples and aggregates a fixed-size neighborhood

Greedy vs beam search decoding: greedy picks best token, beam maintains k candidates

Ever wondered why Google Search sometimes shows you the top results first?

Kolmogorov–Smirnov test

Ever wondered how scientists know if two things are the same or not?