| Notas: | Introduction : the Bayesian method, its benefits and implementation -- Bayesian model choice, comparison and checking -- The major densities and their application -- Normal linear regression, general linear models and log-linear models -- Hierarchical priors for pooling strength and overdispersed regression modelling -- Discrete mixture priors -- Multinomial and ordinal regression models -- Time series models -- Modelling spatial dependencies -- Nonlinear and nonparametric regression -- Multilevel and panel data models -- Latent variable and structural equation models for multivariate data -- Survival and event history analysis -- Missing data models -- Measurement error, seemingly unrelated regressions, and simultaneous eqations. |
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