Parametric Model Training Task
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A Parametric Model Training Task is a model training task that accepts a parametric model family.
- AKA: Parametric Learning, Parametric Model Fitting.
- Context:
- It can range from being a Parametric Statistical Modeling, Parametric Physical Modeling, ...
- Example(s):
- Counter-Example(s):
- See: Function Fitting Task, Linear Regression Task, Statistical Power, Robust Statistics, Formula.
References
2015
- (Wikipedia, 2015) ⇒ http://en.wikipedia.org/wiki/Parametric_statistics Retrieved:2015-1-15.
- Parametric statistics is a branch of statistics which assumes that the data have come from a type of probability distribution and makes inferences about the parameters of the distribution.[1] Most well-known elementary statistical methods are parametric.[2] The difference between parametric model and non-parametric model is that the former has a fixed number of parameters, while the latter grows the number of parameters with the amount of training data. Generally speaking parametric methods make more assumptions than non-parametric methods.[3] If those extra assumptions are correct, parametric methods can produce more accurate and precise estimates. They are said to have more statistical power. However, if assumptions are incorrect, parametric methods can be very misleading. For that reason they are often not considered robust. On the other hand, parametric formulae are often simpler to write down and faster to compute. In some, but definitely not all cases, their simplicity makes up for their non-robustness, especially if care is taken to examine diagnostic statistics.[4]
- ↑ Geisser, S.; Johnson, W.M. (2006) Modes of Parametric Statistical Inference, John Wiley & Sons, ISBN 978-0-471-66726-1
- ↑ Cox, D.R. (2006) Principles of Statistical Inference, Cambridge University Press, ISBN 978-0-521-68567-2
- ↑ Corder; Foreman (2009) Nonparametric Statistics for Non-Statisticians: A Step-by-Step Approach, John Wiley & Sons, ISBN 978-0-470-45461-9
- ↑ Freedman, D. (2000) Statistical Models: Theory and Practice, Cambridge University Press, ISBN 978-0-521-67105-7