Bayesian Modeling Task
(Redirected from Bayesian Modeling)
- output: a Bayesian Model.
- It can range from being Non-Parametric Bayesian Modeling to being Parametric Bayesian Modeling (that require the specification of prior distributions for any unknown parameters).
- It can be solved by a Bayesian Modeling System (that implements a Bayesian Modeling Algorithm).
- See: Bayesian Inference, Model Fitting, Bayesian Hypothesis Testing, Bayesian Variable Selection, Bayesian Statistics, Hyperprior Distribution.
- (Wikipedia, 2016) ⇒ http://wikipedia.org/wiki/Bayesian_statistics#Statistical_modeling Retrieved:2016-2-18.
- The formulation of statistical models using Bayesian statistics has the unique feature of requiring the specification of prior distributions for any unknown parameters. These prior distributions are as integral to a Bayesian approach to statistical modelling as the expression of probability distributions. Prior distributions can be either hyperparameters or hyperprior distributions.
- (Ntzoufras, 2011) ⇒ Ioannis Ntzoufras. (2011). “Bayesian modeling using WinBUGS: An Introduction." Vol. 698. John Wiley & Sons.
- (Wikle et al., 2001) ⇒ Christopher K. Wikle, Ralph F. Milliff, Doug Nychka, and L . Mark Berliner. (2001). “Spatiotemporal Hierarchical Bayesian Modeling Tropical Ocean Surface Winds." Journal of the American Statistical Association 96, no. 454
- (Fernández et al., 1998) ⇒ Carmen Fernández, and Mark FJ Steel. (1998). “On Bayesian Modeling of Fat Tails and Skewness." Journal of the American Statistical Association 93, no. 441