Non-Parametric Model Training Task

A Non-Parametric Model Training Task is a Model Training Task that requires few (if any) assumptions about the system's probability distributions.

• AKA: Nonparameteric Learning.
• Context:
  • It can be solved by a Nonparametric Statistical Modeling System (that implements a Nonparametric Learning algorithm).
  • …
• Counter-Example(s):
  • Parametric Statistical Modeling.
  • Parametric Learning.
• See: Ensemble Learning Task, Parametrization, Probability Distribution, Descriptive Statistics, Statistical Inference, Parametric Statistics, Nonparametric Statistics.

---

---

References

2015

• (Wikipedia, 2015) ⇒ http://en.wikipedia.org/wiki/Nonparametric_statistics Retrieved:2015-1-15.
  • 'Nonparametric statistics are statistics not based on parameterized families of probability distributions. They include both descriptive and inferential statistics. The typical parameters are the mean, variance, etc. Unlike parametric statistics, nonparametric statistics make no assumptions about the probability distributions of the variables being assessed. 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. Note that the non-parametric model is not none-parametric.

2006

• (Wasserman, 2006) ⇒ Larry Wasserman. (2006). “All of Nonparametric Statistics." Springer.

---