Relative likelihood

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Covers iterative weighted least squares, Poisson regression, mixed models, and likelihood ratio statistic.

Explores sparse learning of chemical reaction networks from trajectory data using data-based methods and learning approaches.

Introduces Maximum Likelihood Estimation in econometrics, covering principles, properties, applications, and specification tests.

Explores choice models with latent variables and their estimation process based on likelihood integration.

Covers logistic regression, likelihood function, Newton's method, and classification error estimation.

Covers important sampling for efficient Monte Carlo estimation of expected values using a new distribution to reduce variance.

Explains the E-step and M-step calculations in the Gaussian Mixture Model, including the pseudocode of the EM algorithm.