# 3.3.1 Bayesian network, ISO 22989 Published contents Contents1 Scope2 Normative references3 Terms and definitions4 Abbreviated terms5 AI conceptsClause 3, terms 3.1 Terms related to AI3.1.1 AI agent3.1.2 AI component3.1.3 artificial intelligence3.1.4 artificial intelligence system3.1.5 autonomy3.1.6 application specific integrated circuit3.1.7 automatic3.1.8 cognitive computing3.1.9 continuous learning3.1.10 connectionism3.1.11 data mining3.1.12 declarative knowledge3.1.13 expert system3.1.14 general AI3.1.15 genetic algorithm3.1.16 heteronomy3.1.17 inference3.1.18 internet of things3.1.19 IoT device3.1.20 IoT system3.1.21 knowledge3.1.22 life cycle3.1.23 model3.1.24 narrow AI3.1.25 performance3.1.26 planning3.1.27 prediction3.1.28 procedural knowledge3.1.29 robot3.1.30 robotics3.1.31 semantic computing3.1.32 soft computing3.1.33 symbolic AI3.1.34 subsymbolic AI3.1.35 task3.2 Terms related to data3.2.1 data annotation3.2.2 data quality checking3.2.3 data augmentation3.2.4 data sampling3.2.5 dataset3.2.6 exploratory data analysis3.2.7 ground truth3.2.8 imputation3.2.9 input data3.2.10 label3.2.11 personally identifiable information3.2.12 production data3.2.13 sample3.2.14 test data3.2.15 validation data3.3 Terms related to machine learning3.3.1 Bayesian network3.3.2 decision tree3.3.3 human-machine teaming3.3.4 hyperparameter3.3.5 machine learning3.3.6 machine learning algorithm3.3.7 machine learning model3.3.8 parameter3.3.9 reinforcement learning3.3.10 retraining3.3.11 semi-supervised machine learning3.3.12 supervised machine learning3.3.13 support vector machine3.3.14 trained model3.3.15 training3.3.16 training data3.3.17 unsupervised machine learning3.4 Terms related to neural networks3.4.1 activation function3.4.2 convolutional neural network3.4.3 convolution3.4.4 deep learning3.4.5 exploding gradient3.4.6 feed forward neural network3.4.7 long short-term memory3.4.8 neural network3.4.9 neuron3.4.10 recurrent neural network3.5 Terms related to trustworthiness3.5.1 accountable3.5.2 accountability3.5.3 availability3.5.4 bias3.5.5 control3.5.6 controllability3.5.7 explainability3.5.8 predictability3.5.9 reliability3.5.10 resilience3.5.11 risk3.5.12 robustness3.5.13 stakeholder3.5.14 transparency3.5.15 transparency3.5.16 trustworthiness3.5.17 verification3.5.18 validation3.6 Terms related to natural language processing3.6.1 automatic summarization3.6.2 dialogue management3.6.3 emotion recognition3.6.4 information retrieval3.6.5 machine translation3.6.6 named entity recognition3.6.7 natural language3.6.8 natural language generation3.6.9 natural language processing3.6.10 natural language processing3.6.11 natural language understanding3.6.12 optical character recognition3.6.13 part-of-speech tagging3.6.14 question answering3.6.15 relationship extraction3.6.16 sentiment analysis3.6.17 speech recognition3.6.18 speech synthesis3.7 Terms related to computer vision3.7.1 computer vision3.7.2 face recognition3.7.3 image3.7.4 image recognitionClause 5, concepts 5.1 General5.2 From strong and weak AI to general and narrow AI5.3 Agent5.4 Knowledge5.5 Cognition and cognitive computing5.6 Semantic computing5.7 Soft computing5.8 Genetic algorithms5.9 Symbolic and subsymbolic approaches for AI5.10 Data5.11 Machine learning concepts Home / Vocabulary Terms related to machine learning · 3.3.1 # Bayesian network a probabilistic model that computes probabilities with Bayesian inference over a directed acyclic graph - definition - Defined term - ISO/IEC 22989:2022/FDAmd 1 - Last updated: 2026-10-21 ## What this term settles The term looks ordinary until two teams use it differently. The entry removes that room by naming the thing, its inputs and its limits in one sentence. ## Who it binds Nothing here creates an obligation by itself, and the obligation for Bayesian network arrives when the firm writes the term into a control, a specification or a purchase order. ## What to do in your practice Add Bayesian network to the vocabulary register and to the onboarding pack. A term that is only in a PDF is not controlled; it is controlled when a new joiner can find it and a reviewer can test it. ## Evidence an auditor or regulator asks for - A controlled vocabulary entry for Bayesian network with the citation 3.3.1, an owner and a review date. An auditor can check that Bayesian network is controlled rather than assumed. - A model record that names the algorithm, the training run and the parameters that changed. The row names Bayesian network. Shows Bayesian network in use in the artefact the firm relies on, not only in a glossary. - A training log with the data version, the criteria and the result. The record carries the Bayesian network entry and its date. The model record names the vendor product but not the algorithm or the training run, so the term cannot be traced to an artefact. The gap is tested against Bayesian network. Ask your teamWhere does the firm use the term Bayesian network, and can the owner point to the entry it relies on? ## Common gaps this page catches - Bayesian network is used in the model and training record without a written definition, so two teams can mean different things. - A local synonym has grown up beside Bayesian network and the two are now used interchangeably. ## What to prepare before the amendment lands Brief the owners who use Bayesian network most often, then ask each to return one artefact that shows the entry in use. File the artefacts against the register row. ## How to test it here Take one live artefact rather than a template, find the sentence or field where Bayesian network is used, and put this page beside it. Record the answer, the artefact and the date, because that record is the evidence and a glossary entry on its own is only an intention. ## What this page does not claim The page does not reproduce the standard, certify the firm or predict the wording the amendment will use; it states Bayesian network at the claim level "definition" and stops where the public record stops. ## Common questions What does Bayesian network mean in ISO 22989? a probabilistic model that computes probabilities with Bayesian inference over a directed acyclic graph What evidence should we keep for 3.3.1? A controlled vocabulary entry for Bayesian network with the citation 3.3.1, an owner and a review date. A model record that names the algorithm, the training run and the parameters that changed. The row names Bayesian network. Is Bayesian network a requirement? No: Bayesian network is a definition or clause of a terminology standard, and it becomes binding only when a firm writes it into a policy, a contract or a procedure, or when another standard incorporates it. ## Related units - 3.3.2 decision tree - 3.3.3 human-machine teaming Source. ISO/IEC 22989:2022, Clause 3.3.1, Terms and definitions. Open the ISO public entry. Retrieved 21 October 2026. https://www.iso.org/obp/ui/en/#iso:std:iso-iec:22989:ed-1:v1:en ISO catalogue record 88145, the amendment status, stage and dates. Catalogue record 74296, the base standard. Claim level: definition. The definition is in the public preview. This page states it in our own words and does not reproduce the text. Built from the public ISO record and the public clause 3 entries. No clause text is reproduced. How the record is built. Last updated: 2026-10-21 · Markdown version · Check your vocabulary Source: https://iso-22989-revision-checker.theartofservice.com/glossary/bayesian-network/