Can the Difference of Two Hypergeometric Functions be Expressed as One Term?

In summary, the conversation is about simplifying and reducing the difference between two hypergeometric functions, specifically _2F_1(a,b;c;-x^2) and _2F_1(a+1,b+1;c+1;-x^2). The speaker is interested in finding a simpler expression for this difference, preferably without using hypergeometric functions. They also mention a property of hypergeometric functions and ask for the corresponding expression when keeping the second term constant.
  • #1
BCox
16
0
Hello:

I need to simplify the following if possible

_2F_1(a,b;c;-x^2) - _2F_1(a+1,b+1;c+1;-x^2)


In fact, a= 1/2 and c=3/2 and b>=1. In other words, the difference above that I am interested in is more specifically

_2F_1(.5, b; 1.5; -x^2) - _2F_1(.5+1, b+1; 1.5+1; -x^2)

I know that
Arctan x = x* _2F_1(1/2, 1 ; 3/2; -x^2)
which is a special case of the first term when b=1.

But I am more interested in reducing the difference at the top for any b>=1. Can I express the difference above as one term (and hopefully not as hypergeometric fct)? And how?
 
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  • #2
Using Maple, I get
[itex]
{{}_2F_1(1/2,b;\,3/2;\,-{x}^{2})}-{{}_2F_1(3/2,b+1;\,5/2;\,-{x}^{2})} =
\sum _{k=0}^{\infty }{\frac { \left( -1 \right) ^{k}\Gamma \left( b+k \right) {x}^{2\,k}}{\Gamma \left( b \right) \Gamma \left( k+1 \right) \left( 2\,k+1 \right) }}-\sum _{k=0}^{\infty }3\,{\frac { \left( -1 \right) ^{k}\Gamma \left( 1+b+k \right) {x}^{2\,k}}{\Gamma \left( b+1 \right) \Gamma \left( k+1 \right) \left( 2\,k+3 \right) }}
[/itex]
[itex]
=\sum _{k=0}^{\infty }-{\frac { \left( -1 \right) ^{k}{x}^{2\,k}
\left( 4\,b+6\,k+3 \right) \Gamma \left( b+k \right) }{ \left( 2\,k+
3 \right) \left( 2\,k+1 \right) \Gamma \left( b+1 \right) \Gamma
\left( k \right) }}
=1/15\,{x}^{2} \left( 4\,b+9 \right)
{{}_3F_2(3/2,b+1,2/3\,b+5/2;\,7/2,2/3\,b+3/2;\,-{x}^{2})}
[/itex]
 
  • #3
g_edgar said:
Using Maple, I get
[itex]
{{}_2F_1(1/2,b;\,3/2;\,-{x}^{2})}-{{}_2F_1(3/2,b+1;\,5/2;\,-{x}^{2})} =
\sum _{k=0}^{\infty }{\frac { \left( -1 \right) ^{k}\Gamma \left( b+k \right) {x}^{2\,k}}{\Gamma \left( b \right) \Gamma \left( k+1 \right) \left( 2\,k+1 \right) }}-\sum _{k=0}^{\infty }3\,{\frac { \left( -1 \right) ^{k}\Gamma \left( 1+b+k \right) {x}^{2\,k}}{\Gamma \left( b+1 \right) \Gamma \left( k+1 \right) \left( 2\,k+3 \right) }}
[/itex]
[itex]
=\sum _{k=0}^{\infty }-{\frac { \left( -1 \right) ^{k}{x}^{2\,k}
\left( 4\,b+6\,k+3 \right) \Gamma \left( b+k \right) }{ \left( 2\,k+
3 \right) \left( 2\,k+1 \right) \Gamma \left( b+1 \right) \Gamma
\left( k \right) }}
=1/15\,{x}^{2} \left( 4\,b+9 \right)
{{}_3F_2(3/2,b+1,2/3\,b+5/2;\,7/2,2/3\,b+3/2;\,-{x}^{2})}
[/itex]


Thank you for checking in Maple. Hmmm... two things
1. Mathematica software sometimes gives erroneous analytic solutions for integration. Do we fall into that kind of error w. Maple sometimes?
2. If the above is analytically correct, can we represent the solution as exponential or trig functions?
 
  • #4
We have the property such as this

Hypergeometric2F1[a,b,c,z] = (1-z)^(c-b-a)*Hypergeometric2F1[c-a,c-b,c,z]

If we wanted to keep the 2nd term of the hypergeometric function constant, what would the r.h.s. be?


Hypergeometric2F1[a,b,c,z] = something * Hypergeometric2F1[something,b,something,z]

What would the somethings be?
 

1. What is a hypergeometric distribution?

The hypergeometric distribution is a probability distribution that describes the chance of obtaining a certain number of successes in a specific number of draws from a finite population, without replacement. It is typically used to model situations where the sample size is relatively small compared to the population size.

2. What are the properties of a hypergeometric distribution?

There are several key properties of a hypergeometric distribution, including:

  • The random variable can only take on discrete values.
  • The mean, or expected value, is equal to n(N/K), where n is the number of draws, N is the population size, and K is the number of successes in the population.
  • The standard deviation is equal to sqrt((N-n)(N-K)/(N-1)).
  • The distribution is symmetric when plotted.
  • As n approaches N, the hypergeometric distribution approaches the binomial distribution.

3. How is the hypergeometric distribution different from the binomial distribution?

The hypergeometric distribution is similar to the binomial distribution in that both describe the probability of obtaining a certain number of successes in a fixed number of trials. However, the key difference is that the binomial distribution allows for replacement of the sampled items, while the hypergeometric distribution does not. This means that the binomial distribution can be used in situations where the sample size is relatively large compared to the population size, while the hypergeometric distribution is more appropriate for smaller sample sizes.

4. What are some real-world applications of hypergeometric distribution?

The hypergeometric distribution has many real-world applications, including:

  • In quality control, it can be used to determine the probability of finding a certain number of defective items in a sample from a population of items.
  • In genetics, it can be used to model the probability of obtaining a certain number of individuals with a particular gene in a sample from a population.
  • In ecology, it can be used to estimate the population size of a species based on a sample of individuals.
  • In sports, it can be used to calculate the probability of obtaining a certain number of wins in a season based on the team's performance in previous games.

5. How is the hypergeometric distribution calculated?

The probability of obtaining exactly k successes in a sample of n items from a population of N items with K successes can be calculated with the following formula: P(k successes) = [(K choose k)(N-K choose n-k)] / (N choose n)where (x choose y) represents the number of combinations of x items taken y at a time. This formula can be used to calculate the probability for each possible value of k and then added together to find the total probability for the hypergeometric distribution.

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