This research introduces Probability Engineering as a pragmatic approach to overcome limitations in traditional probabilistic modeling for modern AI systems.

• Treats probability distributions as engineered artifacts that can be modified and reinforced
• Provides a framework for managing high-dimensional parameter spaces and heterogeneous data
• Offers practical techniques to enhance model performance and reliability
• Positions engineering principles as central to advancing modern deep learning

For AI engineers, this paradigm shift provides actionable methods to improve model design, optimization, and deployment in complex real-world applications.

Advancing Deep Learning through Probability Engineering: A Pragmatic Paradigm for Modern AI