Lecture
Discrete Random Variables: Medical Testing
Related lectures (31)
Random Variables and Expected Value
Introduces random variables, probability distributions, and expected values through practical examples.
Introduction to Inference
Covers the basics of probability theory, random variables, joint probability, and inference.
Probability and Statistics
Covers p-quantile, normal approximation, joint distributions, and exponential families in probability and statistics.
Probability and Statistics: Independence and Conditional Probability
Explores independence and conditional probability in probability and statistics, with examples illustrating the concepts and practical applications.
Probability Theory: Midterm Solutions
Covers the solutions to the midterm exam of a Probability Theory course, including calculations of probabilities and expectations.
Probability and Statistics
Introduces probability, statistics, distributions, inference, likelihood, and combinatorics for studying random events and network modeling.
Conditional Probability Distributions
Covers conditional probability distributions and introduces the concept of conditional expected value.
Advanced Probabilities: Random Variables & Expected Values
Explores advanced probabilities, random variables, and expected values, with practical examples and quizzes to reinforce learning.
Probabilistic Linear Regression
Explores probabilistic linear regression, covering joint and conditional probability, ridge regression, and overfitting mitigation.
Conditional Probability: Prediction Decomposition
Explores conditional probability, Bayes' theorem, and prediction decomposition for informed decision-making.