How Complex Should a Model Be?

The Winter Model Fair. Chibany judges the student modeling contest, and every entry poses the same dilemma: the flexible models fit everything — including the noise — while the simple ones miss the signal. This Part is about honest model complexity: the bias–variance tradeoff (and its modern double-descent twist), models that grow with the data (Dirichlet process mixtures), and distributions over entire functions (Gaussian processes) — whose infinite-width story is the on-ramp to Part VIII.

graph LR
    A[Bias–Variance 🤖] --> B[Discrete Bayesian<br>Nonparametrics]
    B --> C[Gaussian<br>Processes 🤖]

Chapters

• The Bias-Variance Dilemma
• Discrete Bayesian Nonparametrics
• Continuous Bayesian Nonparametrics: Gaussian Processes

This project is generously funded by the Japanese Probabilistic Computing Consortium Association (JPCCA).