Hierarchical modeling with gaussian processes |
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| Authors: | Ulrich Menzefricke |
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| Affiliation: | University of Toronto , M5S 3E6, Toronto, Ontario, Canada , Joseph L. Rotman School of Management 105 St, George Street |
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| Abstract: | We formulate a hierarchical version of the Gaussian Process model. In particular, we assume there to be data on several units randomly drawn from the same population. For each unit, several responses are available that arise from a Gaussian Process model. The parameters characterizing the Gaussian Process model for the units are modeled to arise from normal or gamma distributions. Results for two simulations are given that compare the performance of the hierarchical and non-hierarchical models. |
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| Keywords: | Gaussian process growth curves hierarchical model hybrid Monte Carlo Markov chain Monte Carlo prediction |
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