Skip to content

Reason about uncertain process histories

OCBF takes possibly-conflicting reports about what happened in a process, weighs how much to trust each source, and answers questions about what most likely happened — while keeping the uncertainty explicit instead of guessing a single history. You call it from your own code; it is not a data pipeline, user interface, or scheduler.

Run your first assessment Explore capabilities

Try it

Python 3.12 or later is required. From a clone of the repository, with an activated environment (see installation):

python -m pip install -e .
python -m examples.fixed_parameters

The example uses synthetic reports and the core finite reference engine.

Find your way

  • Getting started

    Learn by doing: install the library and follow a complete synthetic assessment.

    Getting started →

  • Concepts

    Understand the workflow, the library objects, and why results carry qualifications.

    Concepts →

  • How-to guides

    Solve one task: interpret reports, choose inference, bound execution, or integrate.

    How-to guides →

  • Reference

    Look up capabilities, configuration, result fields, the glossary, and the API.

    Reference →

  • Development

    Contribute: architecture, validation, and documentation maintenance.

    Development →

One model, explicit handoffs

flowchart LR
    C["Semantic context"] --> M["Canonical model"]
    R["Versioned reports"] --> E["Interpreted evidence"]
    E --> M
    T["Resolved trust"] --> M
    M --> I["Inference"]
    I --> B["Qualified posterior"]
    B --> Q["Process queries"]
    N["Normative reference"] --> Q

Database access, ingestion, credentials, scheduling, and screens belong to the calling application; see the integration boundary.

Understand the answer you receive

Results retain their scope, denominators, scientific identities, evidence qualifications, and numerical assessment. A probability is conditional on the supplied model and evidence. An unavailable quantity remains explicit.

See capabilities and limitations and result meanings before using an estimate in a decision.