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Derivation of the Classification of Orthogonal Sheffer Polynomials I am writing out Meixner's proof from his paper
as presented in this paper and this explanation Assumptions {$A(s)e^{xu(s)}=\sum_{n=0}^{\infty}\frac{P_n(x)}{n!}s^n$} {$t(D)=D+t_2D^2+t_3D^3+\cdots$} {$t(D)P_n(x)=nP_{n-1}(x)$} {$A(s)=\sum_{n=0}^{\infty}\frac{P_n(0)}{n!}s^n$} upon setting {$x=0$} Recurrence relation {$P_{n+1}(x)=(x+l_{n+1})P_n(x) +k_{n+1}P_{n-1}(x)$} {$l_{n+1}=l_1+nl$}, {$k_{n+1}=n(k_2+(n-1)k)$}, {$(l\in\mathbb{R},k_2<0,k\leq 0)$} {$t'(u)=1-lt(u)-kt(u)^2$} {$P_{n+1}=(x+l_1 + nl)P_n(x)+n(k_2+(n-1)k)P_{n-1}(x)$} Differential equation
Explicit formula for {$t(D)$} {$$t(D)=\frac{e^{(\alpha-\beta)D}-1}{e^{(\alpha-\beta)D}-\beta}$$} When {$\beta\rightarrow\alpha$} we have {$t(D)=\frac{D}{1+\alpha D}$}. In the general case we have {$(e^{(\alpha - \beta)D}-1)g(x)=g(x+\alpha - \beta)-g(x)$}. Moments and Distributions From moments to distribution Thanks to John Harland! {$\hat{\omega}(\xi)=\sum_{n=0}^{\infty}\frac{(-2\pi i)^n\mu_n}{n!}\xi^n$} {$\omega(x)=(\check{\hat{\omega}})(x)=\int\hat{\omega}(\xi)e^{2\pi i x\xi}\textrm{d}\xi$} Orthogonality measure {$$\int_{-\infty}^{\infty}e^{xu}\textrm{d}\psi(x)=\frac{1}{A(t(u))}\int_{-\infty}^{\infty}\textrm{d}\psi(x)$$}
Note the progression. In terms of the generating functions, we start by assembling collections of nontrivial terms from the exponential, yielding partitioned orderings. We then switch to the exponential function, yielding partitions of unordered sets. We then collapse further so that n! in the numerator and denominator cancel out, yielding permutations. We then lose the odd terms and have building blocks that give only even terms, transpositions. We then seem to link together transpositions into zigzag permutations, and they manifest steps in a circular order. Think of {$N=\frac{-\gamma}{\alpha\beta}$} as a positive integer. Then the Meixner polynomials are the same as the Kravchuk polynomials. The weight function for the Meixner polynomials is {$w((\alpha-\beta)n)=(\frac{\alpha}{\beta})^n\binom{N}{n}$}. If {$\alpha - \beta \neq 0$} then the entire system is moving with a mean {$pN$} and a variance {$p(p-1)N$}. For Meixner-Pollaczek polynomials the unit of space-time is {$\alpha - \bar{\alpha}=2bi$}. The weight function is {$w((2bi)n)=w(\frac{\alpha}{\bar{\alpha}}|\alpha|n) = (\textrm {cos}\;2n\theta + i\; \textrm{sin}\;2n\theta)\binom{N}{n}$}. This is in terms of {$\theta$} where {$\frac{\alpha}{\bar{\alpha}}=\textrm{cos}2\theta + i\textrm{sin}2\theta$}. References Math Stack Exchange. Generating function for fixed-point free involutions. Classification Consider the space {$\mathcal{P}$} of polynomials with differentiation operator {$D$}. Define the formal power series {$t(s)=s + a_2s^2 + a_3s^3 + \dots$} We have {$t(D)=D+a_2D^2+a_3D^3+\dots$} What can we say about a linear operator {$\Lambda$} for which {$D\Lambda=\Lambda t(D)$} ? Applying to polynomial {$P(x)=p_nx^n+p_{n-1}x^{n-1}+\cdots +p_0$}, we have {$D(\lambda(P(x))=\lambda(t(D)P(x))=\lambda[np_nx^{n-1}+(n-1)p_{n-1}x^{n-2}+\cdots + p_1 + a_2n(n-1)p_nx^{n-2} + a_2(n-1)(n-2)p_{n-1}x^{n-3}+\cdots + a_22p_2 + \cdots + n(n-1)(n-2)\cdots 1 \cdot a_np_n]$} {$=np_nx^{n-1} + [(n-1)p_{n-1}+a_2n(n-1)p_n]x^{n-2}+[(n-2)p_{n-2}+a_2(n-1)(n-2)p_{n-1}+a_3n(n-1)(n-2)(n-3)p_n]x^{n-3}+\cdots$} {$\cdots + [2p_2+a_2\cdot 3\cdot 2p_3 + \cdots + a_{n-1}n!p_{n-1}]x + [p_1 + a_22p_2 + a_33\cdot 2p_3 + \cdots + a_nn!p_n]$} Thus the derivative of {$\lambda (P(x))$} has degree {$n-1$}, which means that {$\lambda(P(x))$} has degree {$n$}, just like {$P(x)$}. Assumption Assume that {$\mu=0$} and thus {$\Lambda P_n(x)=x^n$}. Then {$D\Lambda = \Lambda t(D)$} implies The differential equation {$t(u)$} satisfies the differential equation {$t'(u)=1-\lambda u - \kappa t(u)^2$} |