Expectation of a Fraction of Gaussian Hypergeometric Functions

Click For Summary
SUMMARY

The discussion focuses on finding the expectation of a fraction of Gauss hypergeometric functions, specifically the expression $$E_X\left[\frac{{}_2F_1\left(\begin{matrix}x+a+1\\x+a+1\end{matrix},a+1,c\right)}{{}_2F_1\left(\begin{matrix}x+a\\x+a\end{matrix},a,c\right)}\right]$$. Participants suggest consulting authoritative texts such as "Higher Transcendental Functions" by Erdélyi et al. and the NIST Handbook of Mathematical Functions for relevant identities. The random variable in question is identified as x, and the notation used in the query is clarified as a generalized hypergeometric series.

PREREQUISITES
  • Understanding of Gauss hypergeometric functions
  • Familiarity with the concept of expectation in probability
  • Knowledge of the Pochhammer symbol and its notation
  • Ability to interpret generalized hypergeometric series
NEXT STEPS
  • Study the identities of hypergeometric functions in "Higher Transcendental Functions" by Erdélyi et al.
  • Review the NIST Handbook of Mathematical Functions for hypergeometric function properties
  • Learn about the Pochhammer symbol and its applications in hypergeometric series
  • Explore generalized hypergeometric series and their convergence properties
USEFUL FOR

Mathematicians, statisticians, and researchers in applied mathematics who are working with hypergeometric functions and their expectations.

rafgger
Messages
2
Reaction score
0
TL;DR
In this question I am seeking an expression for the fraction of particular hypergeometric functions and more, their expectation.
I am looking for the expectation of a fraction of Gauss hypergeometric functions.

$$E_X\left[\frac{{}_2F_1\left(\begin{matrix}x+a+1\\x+a+1\end{matrix},a+1,c\right)}{{}_2F_1\left(\begin{matrix}x+a\\x+a\end{matrix},a,c\right)}\right]=?$$

Are there any identities that could be used to simplify or express the fraction?Or wouldn't an idea, how to proceed?

Thank you very much!
 
Physics news on Phys.org
The term "expectation" usually refers to a random variable. What is the variable here?
 
mathman said:
The term "expectation" usually refers to a random variable. What is the variable here?
The random variable is x. Even, if there would be an idea, how to simplify the fraction. Would be most appreciated.
 
Sorry I have no idea. I have never worked with these functions.
 
AFAIK there are many identities and the one you may think relevant is probably in:

see: Erdélyi, Arthur; Magnus, Wilhelm; Oberhettinger, Fritz & Tricomi, Francesco G. (1953). Higher transcendental functions (PDF). Vol. I. New York – Toronto – London: McGraw–Hill Book Company, Inc. ISBN 978-0-89874-206-0. MR 0058756.

Or the NIST handbook:
Olde Daalhuis, Adri B. (2010), "Hypergeometric function", in Olver, Frank W. J.; Lozier, Daniel M.; Boisvert, Ronald F.; Clark, Charles W. (eds.), NIST Handbook of Mathematical Functions, Cambridge University Press, ISBN 978-0-521-19225-5, MR 2723248

I can't be of much help, possibly @Stephen Tashi may know more.
 
rafgger said:
Summary:: In this question I am seeking an expression for the fraction of particular hypergeometric functions and more, their expectation.

I am looking for the expectation of a fraction of Gauss hypergeometric functions.

$$E_X\left[\frac{{}_2F_1\left(\begin{matrix}x+a+1\\x+a+1\end{matrix},a+1,c\right)}{{}_2F_1\left(\begin{matrix}x+a\\x+a\end{matrix},a,c\right)}\right]=?$$

Are there any identities that could be used to simplify or express the fraction?Or wouldn't an idea, how to proceed?

Thank you very much!
I'm not sure how to parse the notation. Typically, I've seen
$$_2F_1(a,b;c;x)=\sum_{k=0}^\infty \frac{(a)_k(b)_k}{(c)_k}\frac{x^k}{k!}$$
where ##(a)_k## denotes the Pochhammer symbol. The stacked notation you use is more indicative of a generalized hypergeometric series:
$$_2F_1\left(\begin{matrix}a_1\text{ } a_2\\b_1\end{matrix};x\right) =\sum_{k=0}^\infty \frac{(a_1)_k(a_2)_k}{(b_1)_k}\frac{x^k}{k!} $$
(which is the same as ##_2F_1(a_1,a_2;b;x)##). But I can't quite figure out your notation. In any case, @jim mcnamara is right. Erdelyi is a good source for hypergeometric identities; I'd add Abramowitz and Stegun to that list.

EDIT: apparently Abramowitz and Stegun has entered the digital age and is now the NIST handbook that @jim mcnamara referred to in his post.
 

Similar threads

  • · Replies 6 ·
Replies
6
Views
2K
  • · Replies 10 ·
Replies
10
Views
4K
  • · Replies 2 ·
Replies
2
Views
2K
  • · Replies 1 ·
Replies
1
Views
2K
  • · Replies 2 ·
Replies
2
Views
1K
  • · Replies 2 ·
Replies
2
Views
2K
  • · Replies 6 ·
Replies
6
Views
4K
  • · Replies 5 ·
Replies
5
Views
2K
  • · Replies 10 ·
Replies
10
Views
2K
  • · Replies 2 ·
Replies
2
Views
2K