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Gaussian processes for inferring latent functions in complex data models

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Heavily influenced by the challenges in local volatility modelling from quantitative finance, we consider inferring a latent function in a probabilistic model of data. In vicinity of the former, estimation is approached by deterministic means, commonly least-squares optimisation. Our contribution is to introduce a probabilistic framework based on Gaussian process priors. We approach inference with Markov chain Monte Carlo and extend some of these techniques to scale with data. We propose an approximation that enables sequential sampling of both latent variables and associated hyperparameters. We demonstrate our approach through the local volatility model in a growing data settings which would otherwise be unfeasible with naive, non-sequential sampling.

This talk is part of the Machine Learning @ CUED series.

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