Mixture distribution

Related lectures (30)

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Explores the Jarzynski Equality through experimental tests on RNA molecules, validating the theory in non-equilibrium conditions.

Covers variational inference and neural networks for classification tasks.

Explores joint distributions, marginal laws, covariance, correlation, and variance properties.

Explores bivariate maxima, parametric models, distributions, and dependence functions in statistics.

Explores modeling neurobiological signals, focusing on spikes, firing rate, multiple state neurons, and parameter estimation.

Explores probability distributions for random variables in air pollution and climate change studies, covering descriptive and inferential statistics.

Covers the concept of density matrix for quantum states and its properties.

Covers spherical and elliptical distributions of random vectors.

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

Covers correlation, covariance, empirical estimates, eigenvalues, normality testing, and factor models.