Introduces probability, statistics, distributions, inference, likelihood, and combinatorics for studying random events and network modeling.
Delves into probability, statistics, paradoxes, and random variables, showcasing their real-world applications and properties.
Covers the calculation of expectation and variance for different types of random variables, including discrete and continuous ones.
Introduces Bayesian estimation, covering classical versus Bayesian inference, conjugate priors, MCMC methods, and practical examples like temperature estimation and choice modeling.
Covers the implementation of Markov Chain Monte Carlo (MCMC) with the Metropolis algorithm for sampling from posterior distributions.
Explores air pollution analysis using wind data, probability distributions, and trajectory models for air quality assessment.
