Biclustering & latent variables: statistical analysis of network data

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Related lectures (30)

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Variance: Independent Random Variables and Bernoulli Trials

Explores Bienaymé's Formula for independent random variables and variance in Bernoulli trials.

Probability and Random Variables: Key Concepts Explained

Explains key concepts in probability, including conditional probability, independence, and random variables, with practical examples to illustrate their applications.

Advanced Probability: Expected Value

Explores expected value in probability theory, including dice rolls and Bernoulli trials.

Probability Theory: Discrete Random Variables

Covers discrete random variables, probability mass function, properties, and binomial distribution with illustrative examples.

Random Variables: Introduction and Bernoulli Trials

Introduces random variables, approximation, convergence, and Bernoulli trials in statistics.

Normal Distribution: Properties and Calculations

Covers the normal distribution, including its properties and calculations.

Statistical analysis of network data

Covers stochastic properties, network structures, models, statistics, centrality measures, and sampling methods in network data analysis.

Probability and Statistics

Covers probability, statistics, independence, covariance, correlation, and random variables.

Elements of Statistics: Probability and Random Variables

Introduces key concepts in probability and random variables, covering statistics, distributions, and covariance.

Convergence of Random Variables

Explores independent and identically distributed random variables, convergence, and distribution properties.