Biclustering & latent variables: statistical analysis of network data

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Related lectures (30)

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Statistical Estimation: Properties and Distributions

Explores statistical parameter estimation, sample accuracy, and Bernoulli variables' properties.

Probability Distributions: Basics and Properties

Covers the basics of probability distributions, including mean, variance, and properties of random variables.

Random Variables: Basics and Examples

Explains random variables, distributions, and Bernoulli trials with coin flip examples.

Probability and Statistics: Fundamental Theorems

Explores fundamental theorems in probability and statistics, joint probability laws, and marginal distributions.

Independence and Covariance

Explores independence and covariance between random variables, discussing their implications and calculation methods.

Law of Large Numbers: Strong Convergence

Explores the strong convergence of random variables and the normal distribution approximation in probability and statistics.

Expectation of a Random Variable

Defines the expectation for random variables, emphasizing the importance of absolute values.

Variance and Independent Random Variables

Covers variance, independent random variables, and their properties, including examples and proofs.

Variance: Definition, Examples, and Theorems

Covers the definition of variance, examples, theorems, and applications in probability theory.

Probability and Stochastic Processes: Fundamentals and Applications

Discusses the fundamentals of probability and stochastic processes, focusing on random variables, their properties, and applications in statistical signal processing.