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Lecture
Mutual Information: Continued
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Related lectures (31)
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Mutual Information: Understanding Random Variables
Explores mutual information, quantifying relationships between random variables and measuring information gain and statistical dependence.
Propagation of Uncertainty: Estimation and Distribution
Discusses estimation and propagation of uncertainty in random variables and the importance of managing uncertainty in statistical analysis.
Describing Data: Statistics and Hypothesis Testing
Covers descriptive statistics, hypothesis testing, and correlation analysis with various probability distributions and robust statistics.
Entropy and Mutual Information
On entropy and mutual information explores quantifying information in data science through probability distributions.
Elements of Statistics: Probability and Random Variables
Introduces key concepts in probability and random variables, covering statistics, distributions, and covariance.
Interpretation of Entropy
Explores the concept of entropy expressed in bits and its relation to probability distributions, focusing on information gain and loss in various scenarios.
Central Limit Theorem: Properties and Applications
Explores the Central Limit Theorem, covariance, correlation, joint random variables, quantiles, and the law of large numbers.
Random Walks and Moran Model in Population Genetics
Explores random walks, Moran model, bacterial chemotaxis, entropy, information theory, and coevolving sites in proteins.
Elements of Statistics: Probability, Distributions, and Estimation
Covers probability theory, distributions, and estimation in statistics, emphasizing accuracy, precision, and resolution of measurements.
Information Measures
Covers information measures like entropy and Kullback-Leibler divergence.
