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Found 8 papers, 1 papers with code
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.
Reconstruction and segmentation from sparse sequential X-ray measurements of wood logs
Thus, here we propose the use of a Dimension reduced Kalman Filter to accumulate information between slices and allow for sufficiently accurate reconstructions for further assessment of the object.
Blind hierarchical deconvolution
Deconvolution is a fundamental inverse problem in signal processing and the prototypical model for recovering a signal from its noisy measurement.
Non-Stationary Multi-layered Gaussian Priors for Bayesian Inversion
In this article, we study Bayesian inverse problems with multi-layered Gaussian priors.
Statistics Theory Statistics Theory
Brexit Risk Implied by the SABR Martingale Defect in the EUR-GBP Smile
We construct a data-driven statistical indicator for quantifying the tail risk perceived by the EURGBP option market surrounding Brexit-related events.
Posterior Inference for Sparse Hierarchical Non-stationary Models
Gaussian processes are valuable tools for non-parametric modelling, where typically an assumption of stationarity is employed.
Computation
Hyperpriors for Matérn fields with applications in Bayesian inversion
We introduce non-stationary Mat\'ern field priors with stochastic partial differential equations, and construct correlation length-scaling with hyperpriors.
Statistics Theory Statistics Theory
