makemyprior: Intuitive Construction of Joint Priors for Variance Parameters in R

makemyprior: Intuitive Construction of Joint Priors for Variance Parameters in R

: Bayesian hierarchical models, robust inference, joint prior distributions, hierarchical variance decomposition, graphical user interface, R.

Priors allow us to robustify inference and to incorporate expert knowledge in Bayesian
hierarchical models. This is particularly important when there are random effects that
are hard to identify based on observed data. The challenge lies in understanding and
controlling the joint influence of the priors for the variance parameters, and makemyprior
is an R package that guides the formulation of joint prior distributions for variances. A
joint prior distribution is constructed based on a hierarchical decomposition of the total
variance in the model along a tree, and takes the entire model structure into account.
Users input their prior beliefs or express ignorance at each level of the tree. Prior beliefs
can be general ideas about reasonable ranges of variance values and need not be detailed
expert knowledge. The constructed priors lead to robust inference and guarantee proper
posteriors. A graphical user interface facilitates construction and assessment of different
choices of priors through visualization of the tree and joint prior. The package aims to
expand the toolbox of applied researchers and make priors an active component in their
Bayesian workflow.

Ingeborg Hem

https://www.jstatsoft.org/article/view/v110i03

Norwegian University of
Science and Technology

August 2024

Fajar bagus W

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