Density estimation
In machine learning, density estimation is defined as an unsupervised learning technique. The purpose of density estimation is to infer the probability density function (PDF), from observations of a random variable.[1] It learns relations among attributes in the data.[2]
Types
- Parametric density estimation: It assumes that the data are from a known family of distributions, such as the normal, lognormal, exponential.[3]
- Non-parametric density estimation:
Terminology
- Estimator
- Consistent estimator
- Unbiased estimator
- Parametric methods
- Non-parametric methods
- Explicit density estimation
- Implicit density estimation