Talk:Positive-definite function

message: The explanation of positive definite function seems to be too narrow here.  
In general a positive definite function is any function f(x,y) with the property that all finite index sets lead to positive definite matrices.  Not, as the article says, just ones of the form f(x-y).  Those of the form f(x-y) are called "stationary" or "homogeneous".
Also this is in the category "complex analysis" which I think may be too limiting.  The value of the function doesn't necessarily have to be complex to have the property of positive definiteness.  
For a reference see, e.g., Matthias Seeger, "Gaussian Processes for Machine Learning".
Baxissimo 06:58, 18 January 2007 (UTC)
replies: text: i would agree that the definition is unnecessarily restrictive. what you call "stationary" is sometimes called "Toeplitz", by analogy with Toeplitz matrices. the contributor(s) who wrote that section probably had a reason for this restriction. Toeplitz kernels is intimately related to moment problems and Fourier transforms of positive measures, as they pointed out. Mct mht 05:30, 1 April 2007 (UTC)
thread_title: Too narrow

message: I don't quite understand -- were you looking for a reference to Bochner's theorem?  Why slap on a "fact" tag?  Is this a particularly controversial statement or are you just seeking aid in finding a reference?
If you simply want aid finding a source, how about , , , or .  Also, if you simply want aid in finding a source, just ask on the talk page; there's no need to mark up the article.  Lunch 15:39, 27 March 2007 (UTC)
thread_title: Bochner's theorem?

message: In statistics, the theorem is usually applied to real functions; the matrix A is then multiplied by a scalar to give a covariance matrix, which must be positive definite. In a statistical context, one does not usually use Fourier terminology and instead one states that f(x) is the characteristic function of a symmetric PDF.
A covariance matrix arises this way?  From what function?  Not the characteristic function of a single random variable.  And positive definiteness of the characteristic function does not imply symmetry of the pdf (it has to be real for that).  72.75.126.69 (talk) 14:56, 19 January 2009 (UTC)
replies: text:  Hi.  the  matrix A arises when considering the covariance matrix of a set of (scalar) observations that are taken at varying points in some space (usually R^2).  Pairs of observations that are close to one another have high correlation and pairs of observations far apart have low correlations.  The difficult bit is to ensure that the overall covariance matrix A is positive definite (my internal  example is measuring temperature over the surface of the Earth.  Pairs of thermometers that are close together are highly correlated).  HTH, Robinh (talk) 15:02, 19 January 2009 (UTC)
replies: text:  Right, I've edited the page...bit clearer now... Robinh (talk) 15:13, 19 January 2009 (UTC)
thread_title: Statistics

message: Should it be: 0<f(0) and 0<=f(x)<f(0) for all x?  — Preceding unsigned comment added by 194.176.105.138 (talk) 16:43, 31 August 2011 (UTC)
replies: text: Indeed, the first definition looks incorrect. Please edit this. 6:48, 27 March 2012 (UTC)

text: As stated, these inequalities are simple consequences of the definition, not part of the definition. But that leaves the definition of positive semidefinite completely unclear: There is no inequality in the definition that can be weakened.--Hagman (talk) 10:07, 21 October 2018 (UTC)
thread_title:  Is the basic definition wrong?

message: The first section gives what it claims is the "most common usage" of the term positive definite. I've done a lot of math and physics and have literally never seen this definition used. Perhaps it is more common in some branches of mathematics, but the claim that it is "most common" seems unjustified. I propose changing the section titles to, say, "Definition 1" and "Definition 2", or some other headings that clarify that both uses are in common usage. The-erinaceous-one (talk) 00:57, 3 August 2022 (UTC)
thread_title:  "Most Common" Definition??