... is particularly important for posterior probabilities, p(a|D), that have multiple maxima distributions. parameter estimation Bayesian MCMC variational Bayes inverse problems Proceedings of the ASME 2021 Heat Transfer Summer Conference HT2021 June 16-18, 2021, Virtual, Online HT2021-62998 THE USE...

... on convergence performance is examined. The present methodology is a combination of two different methods: The Conjugate Gradient Method with Adjoint Problem (CGMAP) and Box-Kanemasu method (BKM). The methodology uses the benefit of CGMAP in handling function estimation problems and BKM for parameter estimation...

... 10 07 2009 The precision of estimates of system performance and parameter estimation is often based upon the standard deviation obtained from the usual equation for the propagation of variances derived from a Taylor series expansion [1]. With increasing computing power, it is often...

... expensive. A solution is the use of Gaussian processes that allows one to create a response surface using easy-to-evaluate functions. This paper describes the use of these ideas for a heat transfer problem. Sensitivity Global Sensitivity Gaussian Processes Parameter Estimation Variability...

... the assumptions that the measurement noise has zero mean and is normally distributed, yielding unbiased and normally distributed parameter estimates. In fact, even for this type of noise, nonlinear functions can lead to bias and nonnormal distributions of estimated properties. We examine these effects and show...