Posterior predictive distribution

Related lectures (30)

Page 3 of 3

Explores non-parametric statistics, Bayesian methods, and linear regression with a focus on kernel density estimation and posterior distribution.

Covers partitioning equilibrium, sorption isotherms, and the role of natural organic matter in environmental chemistry.

Introduces the Relevance Vector Machine as a sparse solution to SVM shortcomings.

Explores optimization and estimation in Monte Carlo methods, emphasizing Bayes-optimal groups and estimators.

Explores ceramic forming methods, emphasizing particle packing, compaction, additives, ejection, and slip casting practical aspects.

Covers the properties of the exponential family and the estimation of parameters.

Covers Bayes estimator, Simulated Annealing, and EM for parameter estimation.

Explores the optimization and penalization of natural cubic splines, including roughness penalties and Bayesian inference.

Explores exponential families, Bernoulli distributions, parameter estimation, and maximum entropy distributions in statistical modeling.

Covers the revision of variables, categorical data analysis, and goodness-of-fit tests.