#sequentialprediction

The classical approach to sequence prediction: counting and smoothing

The problem of predicting what comes next, from compression to language models

Markov processes and tree sources: understanding where sequences come from

The evolution of neural sequence prediction, and how it connects to classical methods

Model averaging over hypotheses, the principled way to handle uncertainty in prediction

The bias-data trade-off in sequential prediction: when to use CTW, n-grams, or neural language models.

Validating Context Tree Weighting through experiments, including a bug that changed everything.

The optimal predictor is incomputable. What we can learn from it anyway.