Bayes factor

Related lectures (29)

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Covers the fundamentals of Bayesian estimation, focusing on the application of Bayes' Theorem in scalar estimation.

Explores Bayes risk, generalization, error rates, and interpolation methods in machine learning.

Covers statistics, experimental design, errors, distributions, implications of sample size, and null results.

Explores mixture models and individual-level parameters in discrete choice scenarios, covering distribution, Bayes theorem, and expected values.

Covers Conditional Maximum Likelihood estimation, contribution to likelihood, and MEV model application in choice-based samples.

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

Covers the transformations of joint continuous densities and their implications on probability distributions.

Introduces key statistical concepts through practical exercises and MATLAB applications.

Explores Bayes' Theorem for defective parts detection, discrete random variables, and distribution functions, with practical examples and exercises.

Explores conditional probability, the law of total probability, Bayes' theorem, and prediction decomposition.