A tutorial on Bayesian inference to identify material parameters in solid mechanics

However, gure 12 also show that the impulse response for ination in the model with freely mobile labor does not atten nearly as much when increasing  towards an equal duration

Indeed, one of the main approaches in stochastic modelling, known as the Monte Carlo method, is to run a (large) number of simulations for each value of the parameters and to

For the propagation of the hybrid (probabilistic and possibilistic) uncertainty representation, the MC technique [22], [23] is combined with the extension principle of fuzzy set

The Hamiltonian Monte Carlo algorithm and a method based on a system of dynamical stochastic differential equations were considered and applied to a simplified pyrolysis model to

One can see that if model uncertainty is described by a random variable coming from a normal distribution either with constant parameters or an input-dependent mean, more

2.3 Plausibility and belief functions Once the reliability engineer has defined the basic belief assignment for each component and the network has com- puted the assignment for

In a fully Bayesian treatment of the probabilistic regression model, in order to make a rigorous prediction for a new data point, it requires us to integrate the posterior

Consistant en six étapes, SPIKES est un protocole ayant pour but de « permettre au clinicien d'accomplir les quatre objectifs les plus importants de l'entretien