A programming paradigm designed to handle uncertainty by specifying probabilistic models and automating the process of inference within these models. It integrates traditional programming with probabilistic modeling, allowing for the creation of systems that can make decisions in uncertain environments.

• Machine learning
• Artificial intelligence
• Data science
• Robotics
• Control systems
• Optimization problems

For example, a probabilistic programming language could be used to build a system that can predict the likelihood of a machine failure based on sensor readings and maintenance history. The system would specify a probabilistic model of the machine’s components, and use inference to update the model based on new sensor readings.

• Managing and evolving the semantic data model