Finite Difference Expressed As a Probability Generating Function

In summary, it seems that we might be able to solve differential equations by generating functions and using properties of the derivatives. However, this is something that is more complicated than just generating functions, and we might need to make up a table of properties to help us solve the differential equation.
  • #1
MisterX
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$$F(z) = \sum_{n=0}^\infty a_n x^n $$
$$\partial_zF(z) = \sum_{n=0}^\infty (n+1)a_{n+1}x^n $$
So, we can begin to piece together some differential equations in terms of generating functions in order to satisfy some discrete recursion relation (which is the desired problem to solve). However I desire something more - perhaps a table of properties akin to what we might find for the eerily similar-yet-different z-transform.
$$a_{n+1} - a_n = g(n) \forall n \to M(F(z), F'(z), \dots) = 0 $$
Can we establish a property for the finite difference for example? What would be ##M## ? Intuitively we expect something like zF(z) + boundary terms.
 
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  • #2
Thanks for the post! Sorry you aren't generating responses at the moment. Do you have any further information, come to any new conclusions or is it possible to reword the post?
 
  • #3
This is like a inverse problem to the Frobenius method of solving differential equations. Maybe someone who has made up a lot of homework problems for that method would have insight.

We could begin by thinking about functions [itex] F(z) [/itex] that are polynomials of degree N instead of infinite series It might also be simpler to think about being given [itex] a_n = G(n) [/itex] and knowing [itex] g(n) [/itex] from the differences of [itex] G(n) [/itex]. Thinking of polynomials as vectors in the usual way, the [itex] a_i [/itex] are the coefficients for basis vectors in the set [itex] \{ 1, z, z^2, ..z^N\} [/itex]. The values of [itex] G(n) [/itex] define a vector. If we want a linear differential equation with constant coefficients we need to represent [itex] G(n) [/itex] as linear combination of the vectors in the set [itex] S [/itex] of vectors representing [itex] \{ F(z), F'(z), F''(z),...F^N(z) \} [/itex]. If we want an DE whose coefficients are polynomials in [itex] z [/itex] then we add more vectors to S such as those representing [itex] \{ zF'(z), z^2F'(z), ... zF''(z), z^2 F''(z)...\} [/itex]

Intuitively, a typical [itex] G(n) [/itex] defined by a vector of values randomly selected by someone creating a homework problem would give enough independent vectors in the set [itex] S [/itex] to include [itex] G(n) [/itex] in its span. But hastily making up homework problems is risky.!
 
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Related to Finite Difference Expressed As a Probability Generating Function

1. What is a finite difference expressed as a probability generating function?

A finite difference expressed as a probability generating function is a mathematical representation of the probability distribution of a discrete random variable. It is used to describe the relationship between the values of a random variable and their corresponding probabilities.

2. How is a finite difference expressed as a probability generating function calculated?

A finite difference expressed as a probability generating function is calculated by taking the difference between the values of the random variable and their corresponding probabilities, and then expressing that difference as a function of the probability generating function.

3. What is the purpose of using a finite difference expressed as a probability generating function?

The purpose of using a finite difference expressed as a probability generating function is to accurately describe the probability distribution of a discrete random variable and to make predictions about future values of the random variable.

4. What are some examples of situations where a finite difference expressed as a probability generating function would be useful?

A finite difference expressed as a probability generating function can be useful in situations where there are a finite number of possible outcomes, such as in gambling, genetics, and finance. It can also be used in statistical analysis and risk management.

5. Are there any limitations to using a finite difference expressed as a probability generating function?

Yes, there are some limitations to using a finite difference expressed as a probability generating function. It assumes that the random variable is discrete, and it can only be used for finite differences. It also does not take into account any external factors that may influence the probability distribution of the random variable.

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