Editing Beta distribution (section)

===Wavelet analysis===
{{Main|Beta wavelet}}

A [[wavelet]] is a wave-like [[oscillation]] with an [[amplitude]] that starts out at zero, increases, and then decreases back to zero. It can typically be visualized as a "brief oscillation" that promptly decays. Wavelets can be used to extract information from many different kinds of data, including&nbsp;– but certainly not limited to&nbsp;– audio signals and images. Thus, wavelets are purposefully crafted to have specific properties that make them useful for [[signal processing]]. Wavelets are localized in both time and [[frequency]] whereas the standard [[Fourier transform]] is only localized in frequency. Therefore, standard Fourier Transforms are only applicable to [[stationary process]]es, while [[wavelet]]s are applicable to non-[[stationary process]]es.  Continuous wavelets can be constructed based on the beta distribution. [[Beta wavelet]]s<ref name="wavelet oliveira">H.M. de Oliveira and G.A.A. Araújo,. Compactly Supported One-cyclic Wavelets Derived from Beta Distributions. ''Journal of Communication and Information Systems.'' vol.20, n.3, pp.27-33, 2005.</ref> can be viewed as a soft variety of [[Haar wavelet]]s whose shape is fine-tuned by two shape parameters α and β.