This paper presents a theoretical framework for modeling the term structure of interest rates, focusing on the discretization of continuous variables to better align with empirical distributions. The authors introduce the concept of a "discretized seed," which is a distribution that naturally leads to an infinite superposition of Markov processes, allowing for a power-law decrease in the empirical probabilities of interest rate variations. This approach avoids the Gaussian-like tail behavior often derived from continuous equations with finite Markov processes. The discretized theoretical distributions are derived from the Federal Reserve System (FRS) data, showing excellent agreement with the tails of the distributions. The results have implications for developing new methods for computing value-at-risk and fixed-income derivative pricing, suggesting the presence of critical exponents related to self-organized systems. The paper also discusses the numerical integration of the discretized equations and the minimization of the $\chi^2$ function to determine the parameters of the seed function. The findings highlight the simplicity and effectiveness of the proposed model in capturing the complex dynamics of interest rate variations.This paper presents a theoretical framework for modeling the term structure of interest rates, focusing on the discretization of continuous variables to better align with empirical distributions. The authors introduce the concept of a "discretized seed," which is a distribution that naturally leads to an infinite superposition of Markov processes, allowing for a power-law decrease in the empirical probabilities of interest rate variations. This approach avoids the Gaussian-like tail behavior often derived from continuous equations with finite Markov processes. The discretized theoretical distributions are derived from the Federal Reserve System (FRS) data, showing excellent agreement with the tails of the distributions. The results have implications for developing new methods for computing value-at-risk and fixed-income derivative pricing, suggesting the presence of critical exponents related to self-organized systems. The paper also discusses the numerical integration of the discretized equations and the minimization of the $\chi^2$ function to determine the parameters of the seed function. The findings highlight the simplicity and effectiveness of the proposed model in capturing the complex dynamics of interest rate variations.