Introduction to Probability TheoryCovers the basics of probability theory, including definitions, calculations, and important concepts for statistical inference and machine learning.
Probability and StatisticsCovers p-quantile, normal approximation, joint distributions, and exponential families in probability and statistics.
Discrete Choice AnalysisIntroduces Discrete Choice Analysis, covering scale, depth, data collection, and statistical inference.
Elements of StatisticsIntroduces key statistical concepts like probability, random variables, and correlation, with examples and explanations.
Conditional ProbabilityExplores conditional probability, the law of total probability, Bayes' theorem, and prediction decomposition.
Probability: ExamplesCovers examples of probability, including Bayes Theorem, independence, and conditional probability.
Law of Total ProbabilityExplores the Law of Total Probability and its applications in real-world scenarios, introducing key concepts in probability theory.
