Rewriting the tenets: A discourse analysis of probabilistic machine learning in aerospace safety and the turn towards real-time, digital-twin-coupled Bayesian risk inference
Department of Mathematics and Statistics, College of Engineering and Physical Sciences, University of New Hampshire, New Hampshire, USA.
Review
International Journal of Scholarly Research in Multidisciplinary Studies, 2026, 06(02), 013–018.
Article DOI: 10.56781/ijsrms.2026.6.2.0015
Publication history:
Received on 24 June 2026; revised on 04 August 2026; accepted on 06 August 2026
Abstract:
Probabilistic Machine Learning (PML) is conventionally introduced through a stable set of tenets: quantify epistemic and aleatoric uncertainty, place priors over parameters, marginalise rather than optimise, and let the posterior speak. This paper does not simply apply those tenets to aerospace crash prevention and passenger safety. Instead, it undertakes a discourse analysis of how the language of PML is being rewritten as the field migrates from offline model-fitting towards real-time, onboard, digital-twin-coupled Bayesian risk inference. Reading the aerospace-safety literature as a discourse rather than a toolbox, this paper argues that three classical tenets are being quietly inverted: the posterior is no longer a static object of belief but a streaming control signal; the prior is no longer a subjective nuisance but a certifiable engineering artefact; and marginalisation is no longer a purely epistemic act but a temporal, resource-bounded one performed against a hard wall-clock. This shift is formalised with a mixed mathematical core drawing on Bayesian filtering, stochastic hazard modelling and constrained decision theory, and it is shown that the emerging discourse coheres around a single wished-for future: an aircraft that continuously estimates its own probability of catastrophic failure and acts, provably and in bounded time, to reduce it. The contribution is a vocabulary and a set of formal objects for that future, offered as a bridge between the mathematician's instinct for rigour and the safety engineer's instinct for assurance.
Keywords:
Probabilistic Machine Learning; Bayesian Filtering; Digital Twin; Aerospace Safety; Hazard Rate; Risk-Bounded Control.
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