Copulas: Properties and Applications

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Related lectures (54)

Statistical Theory: Fundamentals Log in to Mediaspace to watch this video

Covers the basics of statistical theory, including probability models, random variables, and sampling distributions.

Random Vectors and Stochastic Models for Communications Log in to Mediaspace to watch this video

Covers random vectors, joint probability, and conditional probability in communication stochastic models.

Probability Distributions: Central Limit Theorem and Applications Log in to Mediaspace to watch this video

Discusses probability distributions and the Central Limit Theorem, emphasizing their importance in data science and statistical analysis.

Probability Theory: Basics and Applications Log in to Mediaspace to watch this video

Covers the fundamentals of probability theory, including conditional probability, Bayes' rule, and random variables.

Estimating R: Marginal and Conditional Distributions Log in to Mediaspace to watch this video

Covers the estimation of R using bivariate normal distributions and explores the marginal and conditional distributions of X₁ and X₂.

Elements of Statistics: Probability and Random Variables Log in to Mediaspace to watch this video

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

Probability Fundamentals Log in to Mediaspace to watch this video

Introduces fundamental probability concepts, including events, complements, conditional probability, and random variables.

Normal Distribution: Properties and Calculations Log in to Mediaspace to watch this video

Covers the normal distribution, including its properties and calculations.

Conditional Probability: Understanding Events and Their Relationships Log in to Mediaspace to watch this video

Covers conditional probability, illustrating how events influence each other's probabilities and its applications in real-world scenarios.

Expectation Maximization: Learning Parameters Log in to Mediaspace to watch this video

Covers the Expectation Maximization algorithm for learning parameters and dealing with unknown variables.

Stochastic Models for Communications Log in to Mediaspace to watch this video

Covers random vectors, joint probability density, independent random variables, functions of two random variables, and Gaussian random variables.

Prediction Decomposition: Probability and Permutations Log in to Mediaspace to watch this video

Discusses prediction decomposition in probability theory and explores random permutations.

Martingales and Brownian Motion: Leaving Intervals and Maximum Distribution Log in to Mediaspace to watch this video

Explores the average time for a Brownian motion to leave an interval and the maximum distribution.

Gaussian Mixture Models & Noisy Signals Log in to Mediaspace to watch this video

Explores Gaussian mixture models and denoising noisy signals using a probabilistic approach.

Conditional Probability: Definition and Examples Log in to Mediaspace to watch this video

Explains the calculation of conditional probability with definitions and examples.

Bayes' Theorem: Applications and Simulations Log in to Mediaspace to watch this video

Covers the application of Bayes' Theorem in practical reasoning, especially in clinical settings.

Log-Concave Functions Log in to Mediaspace to watch this video

Covers the concept of log-concave functions and their implications in probability distributions and Gaussian correlation inequalities.

Binomial and Poisson Mass Functions Log in to Mediaspace to watch this video

Explores binomial and Poisson mass functions, calculating probabilities and discussing distribution functions of random variables.

Law of Large Numbers: Strong Convergence Log in to Mediaspace to watch this video

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

Topic Models: Latent Dirichlet Allocation Log in to Mediaspace to watch this video

Covers topic models, focusing on Latent Dirichlet Allocation, clustering, GMMs, Dirichlet distribution, LDA learning, and applications in digital humanities.

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