On the relationship between a Gamma distributed precision parameter and the associated standard deviation in the context of Bayesian parameter inference

01/15/2021
by   Manuel M. Eichenlaub, et al.
0

In Bayesian inference, an unknown measurement uncertainty is often quantified in terms of a Gamma distributed precision parameter, which is impractical when prior information on the standard deviation of the measurement uncertainty shall be utilised during inference. This paper thus introduces a method for transforming between a gamma distributed precision parameter and the distribution of the associated standard deviation. The proposed method is based on numerical optimisation and shows adequate results for a wide range of scenarios.

READ FULL TEXT
research
02/05/2018

Fast and accurate approximation of the full conditional for gamma shape parameters

The gamma distribution arises frequently in Bayesian models, but there i...
research
05/01/2021

Bayesian Inference of a Dependent Competing Risk Data

Analysis of competing risks data plays an important role in the lifetime...
research
04/08/2021

Numerical methods and hypoexponential approximations for gamma distributed delay differential equations

Gamma distributed delay differential equations (DDEs) arise naturally in...
research
12/06/2022

Online Bayesian prediction of remaining useful life for gamma degradation process under conjugate priors

Gamma process has been extensively used to model monotone degradation da...
research
03/29/2023

Leveraging joint sparsity in hierarchical Bayesian learning

We present a hierarchical Bayesian learning approach to infer jointly sp...
research
08/27/2019

Handover Optimality in Heterogeneous Networks

This paper introduces a new theoretical framework for optimal handover p...
research
09/10/2020

Virtual Image Correlation uncertainty

The Virtual Image Correlation method applies for the measurement of silh...

Please sign up or login with your details

Forgot password? Click here to reset