Probability and StatisticsDelves into probability, statistics, paradoxes, and random variables, showcasing their real-world applications and properties.
MCMC with MetropolisCovers the implementation of Markov Chain Monte Carlo (MCMC) with the Metropolis algorithm for sampling from posterior distributions.
Probability and StatisticsCovers Simpson's paradox, probability distributions, and real-life examples in probability and statistics.
Maximum Likelihood InferenceExplores maximum likelihood inference, comparing models based on likelihood ratios and demonstrating with a coin example.
Probability and StatisticsIntroduces probability, statistics, distributions, inference, likelihood, and combinatorics for studying random events and network modeling.
