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This paper presents a statistical framework enabling optimal sampling and robust analysis of fatigue data. We create protocols using Bayesian maximum entropy sampling, which build on the staircase and step methods, removing the requirement of prior knowledge of the fatigue strength distribution for data collection. Results show improved sampling efficiency and parameter estimation over the conventional approaches. Statistical methods for distinguishing between distribution types highlight the role of the protocol in model distinction. Experimental validation of the above work is performed, showing the applicability of the methods in laboratory testing.
A new phenomenological technique for using constant amplitude loading data to predict fatigue life from a variable amplitude strain history is presented. A critical feature of this reversal-by-reversal model is that the damage accumulation is inheren
In this paper, we propose a Bayesian Hypothesis Testing Algorithm (BHTA) for sparse representation. It uses the Bayesian framework to determine active atoms in sparse representation of a signal. The Bayesian hypothesis testing based on three assump
We consider sequential hypothesis testing between two quantum states using adaptive and non-adaptive strategies. In this setting, samples of an unknown state are requested sequentially and a decision to either continue or to accept one of the two hyp
The problem of verifying whether a multi-component system has anomalies or not is addressed. Each component can be probed over time in a data-driven manner to obtain noisy observations that indicate whether the selected component is anomalous or not.
Reliable models of the thermodynamic properties of materials are critical for industrially relevant applications that require a good understanding of equilibrium phase diagrams, thermal and chemical transport, and microstructure evolution. The goal o