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Gelman [8] studid inverse gamma distribution as a prior distributions for variance parameters in hierarchical models.
Specifically, we test whether changes in variance, parameters and mean are significant in the respective models.
In this Section, both channel and variance parameters are being considered.
The mean and the variance parameters for unmatched distributions remain the same.
Non-stationary variance parameters were modelled as functions of discrete or continuous auxiliary variables.
The noise variance parameters and were initialized to, thus assuming a total output noise variance that is 0.5 initially.
The IBP concentration parameter has a gamma prior, and the variance parameters of A and E have inverse gamma priors.
There are totally five fixed effects and six variance parameters (five from random effects and one from measurement error).
For n.comp = 2 or 3 estimation of different variance parameters for each component is allowed.
Support for Van Valen's [5] niche variation hypothesis was evaluated by comparing models that allowed variance parameters to vary spatially (by region) to models that shared variance parameters among regions.
Estimation of regression coefficients and variance parameters is carried out using iteratively weighted least squares and approximate restricted maximum likelihood.
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