Probability Value Estimation Task
(Redirected from Density estimation)
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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, 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.