Learning library

Probability

Sensors are noisy and outcomes are uncertain. These lessons cover conditional probability, Bayesian updating, common distributions, and Markov chains, each grounded in a robot that has to act on incomplete information.

5 lessons, in library order. Search within probability.

  1. Foundations / 8 min read

    Conditional probability: reading a robot sensor

    Learn conditional probability with an interactive robot sensor example. Work through the formula, Bayes' rule, base rates, and common mistakes using counts.

  2. Foundations / 8 min read

    Bayesian inference: learning a robot's grasp success rate

    Learn Bayesian inference through a robot grasp example. Update a Beta prior with successes and failures, then distinguish uncertainty from prediction.

  3. Foundations / 10 min read

    Logistic regression: turn a score into a probability

    Explore logistic regression with synthetic robot observations. Calculate sigmoid probabilities and log loss, then change a threshold and inspect the confusion matrix.

  4. Foundations / 9 min read

    Markov chains: transitions and stationary distributions

    Learn Markov chains with a robot example. Explore transition matrices, stationary distributions, periodic chains, and absorbing states with an interactive model.

  5. Foundations / 9 min read

    Laplace distribution: model sensor error and tolerance

    Learn the Laplace distribution through sensor error. Explore location, scale, density, interval probabilities, and the link between absolute error and median fitting.