Bayesian and Probabilistic Inference Frameworks for Intelligent Parameter Tuning in Next-Generation Computational Systems

Abstract

Emerging computational systems increasingly rely on algorithmic components whose performance depends on high-dimensional and context-sensitive parameters. As workloads diversify and hardware architectures evolve, manual tuning rarely scales and static heuristics degrade when operational conditions change. Bayesian and probabilistic inference offer a principled language for representing uncertainty about latent performance functions, for combining heterogeneous sources of evidence, and for making sequential decisions that trade off exploration and exploitation. This paper develops a neutral and technically detailed account of how Bayesian modeling and related probabilistic frameworks enable intelligent parameter tuning in next-generation computational environments, including accelerators, distributed runtimes, and adaptive data processing pipelines. The exposition emphasizes modular generative models for performance signals, posterior inference mechanisms suitable for low-latency control loops, and decision-theoretic criteria aligned with service-level objectives and safety constraints. Sequential design techniques, variational approximations, and probabilistic programming tools are described in the context of real-time feedback and multi-fidelity measurements, while robustness is treated through risk-sensitive objectives and distribution shift diagnostics. The presentation avoids domain-specific claims and restricts itself to model constructions, algorithmic templates, and analysis strategies that can be composed with system-level scheduling and monitoring. The discussion also highlights implementation considerations such as amortized inference, streaming updates, and compute–communication trade-offs on heterogeneous platforms. The overall aim is to delineate precise probabilistic formulations for tuning problems, to articulate their computational realizations, and to summarize evaluation protocols that quantify uncertainty-aware adaptation without presupposing particular benchmarks or vendor-specific stacks.