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.
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- 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, 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.
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]\displaystyle{ f(\bullet) }[/math] of a random variable [math]\displaystyle{ X }[/math].