Statistical methods and models commonly involve multiple parameters that can be regarded as related or connected in such a way that the problem implies a dependence of the … See more

The assumed occurrence of a real-world event will typically modify preferences between certain options. This is done by modifying the degrees of belief attached, by an individual, to the events defining the options.
Suppose in a study … See more

Components
Bayesian hierarchical modeling makes use of two important concepts in deriving the posterior distribution, namely:
1. See more

The usual starting point of a statistical analysis is the assumption that the n values $${\displaystyle y_{1},y_{2},\ldots ,y_{n}}$$ are … See more

The framework of Bayesian hierarchical modeling is frequently used in diverse applications. Particularly, Bayesian nonlinear mixed-effects models have recently received significant attention. A basic version of the Bayesian nonlinear mixed-effects … See more