Calibrating Expert Assessments Using Hierarchical Gaussian Process Models

Calibrating Subjective Probabilities Using Hierarchical Bayesian Models

Abstract. A body of psychological research has examined the correspondence between a judge’s subjective probability of an event’s outcome and the event’s actual outcome. The research generally shows that subjective probabilities are noisy and do not match the “true” probabilities. However, subjective probabilities are still useful for forecasting purposes if they bear some relationship to true ...

Hierarchical Gaussian Process Regression

We address an approximation method for Gaussian process (GP) regression, where we approximate covariance by a block matrix such that diagonal blocks are calculated exactly while off-diagonal blocks are approximated. Partitioning input data points, we present a two-layer hierarchical model for GP regression, where prototypes of clusters in the upper layer are involved for coarse modeling by a GP...

Gaussian Semiparametric Analysis Using Hierarchical Predictive Models

The Hierarchical Predictive Model (HPM) is a semiparametric mixed model where the fixed effects are fit with a user-specified non-parametric component. This approach extends current spline-based semiparametric mixed model formulations, allowing for more flexible nonparametric estimation. Greater adaptability simplifies model specification making it easier to analyze data sets with large numbers...

Hierarchical Gaussian Process Mixtures for

Discovering Hierarchical Process Models Using ProM

Process models can be seen as “maps” describing the operational processes of organizations. Traditional process discovery algorithms have problems dealing with fine-grained event logs and lessstructured processes. The discovered models (i.e., “maps”) are spaghettilike and are difficult to comprehend or even misleading. One of the reasons for this can be attributed to the fact that the discovere...