# Continuous Probability Function Modeling Task

A Continuous Probability Function Modeling Task is an continuous model-based learning task/probability function modeling task that is a continuous function modeling task to produce a continuous probability function structure.

**AKA:**Continuous Probability Function Creation.**Context:**- It can range from being a Heuristic Continuous Probability Function Modeling Task to being a Data-Driven Continuous Probability Function Modeling Task.
- It can be solved by a Continuous Probability Function Modeling System (that implements a Continuous Probability Function Modeling Algorithm).
- It can be solve by a Density Function Estimation System (that implements a Probability Density Function Estimation Algorithm).

Probability Density Estimation]].

- It can range from being a Parametric Probability Density Estimation to being Non-Parametric Probability Density Estimation.

**Example(s):****Counter-Example(s):****See:**Density Estimator; Kernel Methods; Locally weighted Regression for Control; Nearest Neighbor; Probability Distribution Estimation Task, Unsupervised Learning Task.

## References

### 2015

- (Wikipedia, 2015) ⇒ http://en.wikipedia.org/wiki/Density_estimation Retrieved:2015-2-14.
- In probability and statistics,
**density estimation**is the construction of an estimate, based on observed data, of an unobservable underlying probability density function. The unobservable density function is thought of as the density according to which a large population is distributed; the data are usually thought of as a random sample from that population.A variety of approaches to density estimation are used, including Parzen windows and a range of data clustering techniques, including vector quantization. The most basic form of density estimation is a rescaled histogram.

- In probability and statistics,

### 2011

- (Sammut, 2011d) ⇒ Claude Sammut. (2011). “Density Estimation.” In: (Sammut & Webb, 2011) p.270

### 2009

- http://sfb649.wiwi.hu-berlin.de/fedc_homepage/xplore/tutorials/xlghtmlnode33.html#smoo_compkde
- QUOTE: The goal of density estimation is to approximate the probability density function [math]f(\bullet)[/math] of a random variable [math]X[/math].