Linear mixed models (LMMs) are a powerful and established tool for studying genotype–phenotype relationships. A limitation of the LMM is that the model assumes Gaussian distributed residuals, a ...

The predictive likelihood is useful for ranking models in forecast comparison exercises using Bayesian inference. We discuss how it can be estimated, by means of marginalization, for any subset of the ...

We propose an affine extension of the linear Gaussian term structure model (LGM) such that the instantaneous covariation of the factors is given by an affine process on semidefinite positive matrixes.

The generation of synthetic market data is widely seen as one of the most promising applications of sophisticated artificial intelligence models, such as generative adversarial networks (GANs) and ...

ABSTRACT Linear stochastically forced models have been found to be competitive with comprehensive nonlinear weather and climate models at representing many features of the observed covariance ...