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Found 8 papers, 2 papers with code
Spatiotemporal Besov Priors for Bayesian Inverse Problems
Fast development in science and technology has driven the need for proper statistical tools to capture special data features such as abrupt changes or sharp contrast.
Bayesian Learning via Q-Exponential Process
In this work, we generalize the $q$-exponential distribution (with density proportional to) $\exp{(- \frac{1}{2}|u|^q)}$ to a stochastic process named $Q$-exponential (Q-EP) process that corresponds to the $L_q$ regularization of functions.
Sampling Constrained Continuous Probability Distributions: A Review
The problem of sampling constrained continuous distributions has frequently appeared in many machine/statistical learning models.
Scaling Up Bayesian Uncertainty Quantification for Inverse Problems using Deep Neural Networks
To address this issue, several methods based on surrogate models have been proposed to speed up the inference process.
Learning Temporal Evolution of Spatial Dependence with Generalized Spatiotemporal Gaussian Process Models
For this purpose, we propose a novel Bayesian nonparametric method based on non-stationary spatiotemporal Gaussian process (STGP).
Methodology
Earth System Modeling 2.0: A Blueprint for Models That Learn From Observations and Targeted High-Resolution Simulations
Climate projections continue to be marred by large uncertainties, which originate in processes that need to be parameterized, such as clouds, convection, and ecosystems.
Sampling constrained probability distributions using Spherical Augmentation
In this paper, we propose a novel augmentation technique that handles a wide range of constraints by mapping the constrained domain to a sphere in the augmented space.
Split Hamiltonian Monte Carlo
With the splitting technique, only the slowly-varying part of the energy needs to be handled numerically, and this can be done with a larger stepsize (and hence fewer steps) than would be necessary with a direct simulation of the dynamics.
Computation
