# Probability Value Estimation Task

(Redirected from density estimation)

A Probability Value Estimation Task is a data-driven estimation task that requires a probability value.

**AKA:**Probability Prediction.**Context:**- It can be supported by a Continuous Probability Function Modeling Task.
- It can (typically) be a Probability Density Function Learning Task.

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

## 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,