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Log-Gaussian Gamma Processes for Training Bayesian Neural Networks in Raman and CARS Spectroscopies
We train two Bayesian neural networks to estimate parameters of the gamma process which can then be used to estimate the underlying Raman spectrum and simultaneously provide uncertainty through the estimation of parameters of a probability distribution.
Mixtures of Gaussian Process Experts with SMC$^2$
Gaussian processes are a key component of many flexible statistical and machine learning models.
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