probabilistic-data-handling

To estimate missing data, you can sample possible values and return probability distributions. This approach generates a structured ThingProbability schema with value, likelihood, sample count, and confidence fields rather than defaulting to unknown.

Probabilistic data handling is a pattern that samples possible values to build distributions when input data is incomplete. It uses a ThingProbability schema to represent outcomes and their likelihoods, showing what is known versus what requires exact data.

You generate probability distributions by passing incomplete input data to a sampling engine that iterates over possibilities. The calculate_something main handler returns structured probabilistic results alongside an accuracy indicator.

Yes, you can track data point stability using the optional DataPointStability type. This feature monitors whether a specific data point remains stable across multiple sampling iterations.

Use probabilistic estimates when modules must provide intelligent estimates for incomplete fields, such as unknown birth times. It helps distinguish what is currently known from what requires exact data collection.

No external dependencies or libraries are required. The pattern defines its own ThingProbability schema, DataPointStability type, and internal sampling functions to process incomplete data independently.