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Related lectures (28)

Introduces key concepts in probability and statistics, such as events, Venn diagrams, and conditional probability.

Covers strain-displacement relations, simplifications, constitutive relations, equilibrium equations, and circular rings.

Explores conditional probability, Bayes' theorem, and prediction decomposition for informed decision-making.

Explores the Frenet-Serret formulas for curvature and torsion in three-dimensional space.

Introduces key concepts in probability and statistics, covering random experiments, events, intersections, unions, and more.

Covers fundamental concepts in probability and statistics, including the law of total probability, Bayes' theorem, and independence of events.

Guides students through a mock exam session and provides insights into solving probability and discrete mathematics exercises.

Covers stiffness, strength, strain, stress, and strain measurement in structural mechanics.

Covers exercises on Bayes' Theorem, moment generating functions, photon counts, disease probabilities, and distribution properties.

Introduces probability, statistics, distributions, inference, likelihood, and combinatorics for studying random events and network modeling.

Explores dependence, correlation, and conditional expectations in probability and statistics, highlighting their significance and limitations.

Covers the equations of linear elasticity for static problems with small strain, emphasizing uniqueness of solutions and Navier's Equations.

Explores the short history of probability and basic concepts like sample space and event.

Covers the deformation of plates in pure bending, total strain energy, and applications like wrinkling.

Explores probabilistic linear regression, covering joint and conditional probability, ridge regression, and overfitting mitigation.

Explores independence and conditional probability in probability and statistics, with examples illustrating the concepts and practical applications.

Covers fundamental concepts of probability and statistics, focusing on data analysis, graphical representation, and practical applications.

Introduces key statistical concepts through practical exercises and MATLAB applications.