Statistical Modeling Task
(Redirected from Probabilistic modeling)
A Statistical Modeling Task is a model-based learning task where the predictor function is a probability function (typically from a statistical model family).
- AKA: Probabilistic Data Analysis, Probabilistic Model Training.
- Context:
- It can be solved by a Statistical Modeling System with a statistical modeling capability (that applies a statistical modeling algorithm).
- It can range from being a Parametric Statistical Modeling Task to being a Non-Parametric Statistical Modeling Task.
- It can range from being a Manual Statistical Modeling Task to being an Automated Statistical Modeling Task.
- It can range from being an Exploratory Statistical Modeling Task to being a Confirmatory Statistical Modeling Task.
- Example(s):
- Counter-Example(s):
- See: Probabilistic Analysis, Data Mining, Mathematical Modeling, Probabilistic Language Modeling.
References
2003
- (Davison, 2003) ⇒ Anthony C. Davison. (2003). “Statistical Models." Cambridge University Press. ISBN:0521773393
- QUOTE: The key idea in statistical modelling is to treat the data as the outcome of a random experiment.
The fundamental idea of statistical modelling is to treat data as the observed values of random variables.
- QUOTE: The key idea in statistical modelling is to treat the data as the outcome of a random experiment.
1999
- (Hoeting et al., 1999) ⇒ Jennifer A. Hoeting, David Madigan, Adrian E. Raftery, and Chris T. Volinsky. (1999). “Bayesian Model Averaging: A Tutorial.” In: Statistical science.
- QUOTE: Standard statistical practice ignores model uncertainty. Data analysts typically select a model from some class of models and then proceed as if the selected model had generated the data.