A Bayesian Method for Planning Accelerated Life Testing PROJECT TITLE :A Bayesian Method for Planning Accelerated Life TestingABSTRACT:During this paper, a Bayesian criterion is proposed based mostly on the expected Kullback-Leibler divergence between the posterior and the previous distributions of the parameters of interest. We have a tendency to decision the Bayesian criterion the reference optimality criterion, that is to find an optimal arrange to maximise the amount of information from the data. A giant-sample approximation is used to simplify the formula to get optimal plans numerically. As a result of optimal plans based mostly on reference optimality criterion don't rely on the sample size, a modified reference optimality criterion is proposed. We offer numerical examples using the Weibull distribution with type I censoring to illustrate the strategies, and to look at the influence of the prior distribution, censoring time, and sample size. We have a tendency to additionally compare our strategies with other criteria through Monte Carlo simulation. Did you like this research project? To get this research project Guidelines, Training and Code... Click Here facebook twitter google+ linkedin stumble pinterest Fully Distributed Social Welfare Optimization With Line Flow Constraint Consideration Decoupling Capacitor Topologies for TSV-Based 3-D ICs With Power Gating