In cases where field (or experimental) measurements are not available, computer models can model real physical or engineering systems to reproduce their outcomes. They are usually calibrated in light ...

In Bayesian divergence time estimation methods, incorporating calibrating information from the fossil record is commonly done by assigning prior densities to ancestral nodes in the tree. Calibration ...

The paper presents a Bayesian framework for the calibration of financial models using neural stochastic differential equations (neural SDEs), for which we also formulate a global universal ...

The authors consider a general calibration problem for derivative pricing models, which they reformulate into a Bayesian framework to attain posterior distributions for model parameters. They then ...

This innovative framework accounts for both parameter uncertainty and discrepancy, key issues that affect prediction accuracy of digital twins of automated material handling systems in semiconductor ...

With the continuous advancement of photoelectric performance in major equipment and advanced instruments, traditional optical elements are increasingly inadequate to meet the demands of modern systems ...

In the last decades we entered in an era where, in various fields, a huge amount of data is becoming available; ecology and forest sciences are not the exception. Field measurements, eddy-covariance ...