Bayesian experimental design (BED) is a framework that uses statistical models and decision making under uncertainty to optimise the cost performance of scientific experiment. Sequential BED, as opposed static considers scenario where we can sequentially update our beliefs about model parameters through data gathered in A class particular interest for natural medical sciences are implicit model...

Increased observability of power distribution networks can reveal signs incipient faults which develop into costly and unexpected plant failures. While low-cost sensing communications infrastructure is facilitating this, it also highlighting the complex nature fault signals, a challenge entails precisely extracting anomalous regions from continuous data streams before classifying underlying sig...

For ECG interpretation, the detection and delineation of P and T waves are challenging tasks. This paper proposes sequential Bayesian methods for simultaneous detection, threshold-free delineation, and waveform estimation of P and T waves on a beat-to-beat basis. By contrast to state-of-the-art methods that process multiple-beat signal blocks, the proposed Bayesian methods account for beat-to-b...

This paper presents a computational framework for uncertainty characterization and propagation, and sensitivity analysis under the presence of aleatory and epistemic uncertainty, and develops a rigorous methodology for efficient refinement of epistemic uncertainty by identifying important epistemic variables that significantly affect the overall performance of an engineering system. The propose...