AI Systems · Mathematics and Logic · Systems and Thinking
Deterministic and Probabilistic Systems
A deterministic process is expected to reproduce the same result when its declared input, prior state, rules, versions, dependencies, and execution conditions are the same. A probabilistic process represents or produces outcomes using probabilities and may express variation or a distribution of possibilities. One larger system can contain both deterministic and probabilistic parts.
Sources & review
Evidence and review
How Atlas supports this record.
Atlas uses original Kinesema wording. These public sources support narrow parts of the record; they are not endorsements or proof of claims outside the roles shown below.
- Review
- Human reviewed
- AI assistance
- Yes—human reviewed
- Last reviewed
- 2026-08-23
State Chart XML (SCXML): State Machine Notation for Control Abstraction
World Wide Web Consortium
- Used here for
- One concrete state-machine execution model with active-state configurations, transitions, ordered processing, and deterministic behavior under its declared conditions.
- Source use
- Paraphrased from source
- Source type
- W3C Recommendation
Probability Distribution — Glossary
National Institute of Standards and Technology
- Used here for
- A probability distribution assigns probabilities to possible outcomes of a random variable within the terminology scope of its named NIST sources.
- Source use
- Paraphrased from source
- Source type
- official terminology index pointing to named NIST source publications
Artificial Intelligence Risk Management Framework (AI RMF 1.0)
National Institute of Standards and Technology
- Used here for
- Wider AI-system and deployment context, lifecycle evaluation, human and organizational roles, and the need to test and monitor system behavior and interpret outputs in context.
- Source use
- Paraphrased from source
- Source type
- official government risk-management framework; version 1.0; published 2023
This is a concise first-release record, not a complete course. Examples, terminology, and reviewed connections can grow through later public projections.
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Connected concepts
These are reviewed Atlas relationships, not suggestions inferred from page order or visual proximity. Open a boundary only when you want the extra detail.
Connects to
Computing and AI Systems
A computing or AI system may combine deterministic validation, routing, or transformation with probabilistic models or decision components.
Connection boundary
This relationship does not classify every AI component as probabilistic, every software component as deterministic, or one mixture as universally preferable.
Connects to
Model Training and Inference
Model training and inference may use probabilistic methods while operating inside deterministic software contracts, data pipelines, validation rules, and presentation boundaries.
Connection boundary
Deterministic surrounding code does not make a model output automatically correct, and probabilistic model behavior does not describe the entire system.
Connects to
State Machines and Transitions
A state-machine contract can define deterministic transition selection under stated conditions, while other systems may attach probabilities to states, events, or possible transitions.
Connection boundary
This relationship does not make every state machine deterministic or define one universal probabilistic state model.