Abstract Multi-fidelity modeling and calibration are data fusion tasks that ubiquitously arise in engineering design. However, there is currently a lack of general techniques can jointly fuse multiple sets with varying fidelity levels while also estimating parameters. To address this gap, we introduce novel approach that, using latent-map Gaussian processes (LMGPs), converts into latent space l...

Latent Gaussian models are flexible models that are applied in several statistical applications. When posterior marginals or full conditional distributions in hierarchical Bayesian inference from these models are not available in closed form, Markov chain Monte Carlo methods are implemented. The component dependence of the latent field usually causes increase in computational time and divergenc...

Distributions over matrices with exchangeable rows and infinitely many columns are useful in constructing nonparametric latent variable models. However, the distribution implied by such models over the number of features exhibited by each data point may be poorly-suited for many modeling tasks. In this paper, we propose a class of exchangeable nonparametric priors obtained by restricting the do...