Placeholder chapter page. Sections are produced by the book-skills pipeline.
Sections
- 4.1 Random variables, distributions, and moments for signals
- 4.2 Estimators, bias-variance, and maximum likelihood
- 4.3 Bayesian inference and priors
- 4.4 Aleatoric vs epistemic uncertainty (introduced early, used everywhere)
- 4.5 Information, entropy, and mutual information
- 4.6 Hypothesis testing and detection theory
- 4.7 Monte Carlo and sampling basics
Lab 4
estimate sensor noise models from data and propagate uncertainty through a simple pipeline.