Systems, apparatus, and methods for generating prediction sets based on a known set of features

An apparatus comprising

• a processor configured to
• identify a plurality of features associated with information of one or more entities;
• access a probability distribution store comprising a plurality of defined numerical ranges as potential possibilities for being paired with the plurality of features of the one or more entities;
• determine a plurality of first probability distributions for each of the defined numerical ranges indicating probabilities that each defined numerical range is assigned to each entity having one or more of the features;
• determine a plurality of second probability distributions for each of the defined numerical ranges indicating probabilities that each defined numerical range is assigned to each entity having one or more additional features;
• determine a merged probability distribution using a probabilistic classifier, based on the plurality of first probability distributions and the plurality of second probability distributions;
• determine and store one or more prediction sets based on the merged probability distribution, including generating the one or more prediction sets based on the merged probability distribution by using a Monte Carlo procedure to generate random sampling numbers, wherein the one or more prediction sets comprise a plurality of prediction values corresponding to the one or more entities;
• select one or more content items to display on a device interface as selected with each entity based on the one or more prediction sets;
• and
• display the one or more content items on the device interface.

A non-transitory computer readable storage medium configured to store instructions that when executed cause a processor to perform:

• identifying a plurality of features associated with information of one or more entities;
• accessing a probability distribution store comprising a plurality of defined numerical ranges as potential possibilities for being paired with the plurality of features of the one or more entities;
• determining a plurality of first probability distributions for each of the defined numerical ranges indicating probabilities that each defined numerical range is assigned to each entity having one or more of the features;
• determining a plurality of second probability distributions for each of the defined numeric al ranges indicating probabilities that each defined numerical range is assigned to each entity having one or more additional features;
• determining a merged probability distribution using a probabilistic classifier, based on the plurality of first probability distributions and the plurality of second probability distributions;
• determining and storing one or more prediction sets based on the merged probability distribution, including generating the one or more prediction sets based on the merged probability distribution by using a Monte Carlo procedure to generate random sampling numbers, wherein the one or more prediction sets comprise a plurality of prediction values corresponding to the one or more entities;
• selecting one or more content items to display on a device interface as selected with each entity based on the one or more prediction sets;
• and
• displaying the one or more content items on the device interface.