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COMPUTER-BASED SYSTEMS FOR DATA DISTRIBUTION ALLOCATION UTILIZING MACHINE LEARNING MODELS AND METHODS OF USE THEREOF

Abstract:

Systems and methods of the present disclosure enable distribution modelling and forecasting for populations and sub-populations of entities by employing a processor to receive a numerical data history for a population of entities, with the numerical data history including a series of activity-related quantity indices through time and the population of entities including sub-populations. The processor determines a combination of normal distributions approximating an index distribution for the sub-population of the entities based on the series of activity-related quantity indices, where the normal distributions are centered around a respective mean quantity value of a respective sub-population. The processor uses the normal distributions to eliminate simulations by using a Bayesian model to approximate an inferred index distribution for a particular sub-population. The processor determines at least one inferred statistical value based on the inferred index distribution.