Tag: statistics
-
Kernel-Embedded Gaussian Processes for Bayesian Computation and Model Evaluation
(Work in Progress) It had been a while since I had worked on a technical blog post, so I wanted to get something out there. I have been playing around with a lot of concepts in my head centering on Gaussian processes and using them for Bayesian computation. Most of the time when I write…
-
Decision Theory for Large-Scale Outlier Detection Using Aleatoric Uncertainty
The content discusses aleatoric uncertainty in Bayesian neural networks and its application to outlier detection. By leveraging decision theory, the author explores how modeling uncertainties in parameters and data generating mechanisms can enhance outlier classification. This involves formulating loss functions and employing Bayesian false discovery rate strategies for effective threshold setting.
-
Bayesian Inversion
This post discusses Bayesian inversion as a probabilistic method for solving inverse problems in geophysics. It emphasizes its advantages over deterministic methods and explores the theoretical foundations. Key concepts include parameter influence, uncertainty in solutions, and the implications of observations on Bayesian analysis, particularly in defining closeness through the parameters of the problem.