منابع مشابه
Hierarchical models of variance sources
In many models, variances are assumed to be constant although this assumption is known to be unrealistic. Joint modelling of means and variances can lead to infinite probability densities which makes it a difficult problem for many learning algorithms. We show that a Bayesian variational technique which is sensitive to probability mass instead of density is able to jointly model both variances ...
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Bayesian hierarchical models typically involve specifying prior distributions for one or more variance components. This is rather removed from the observed data, so specification based on expert knowledge can be difficult. While there are suggestions for ‘default’ priors in the literature, often a conditionally conjugate inverse-gamma specification is used, despite documented drawbacks with thi...
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In parametric hierarchical models, it is standard practice to place mean and variance constraints on the latent variable distributions for the sake of identifiability and interpretability. Because incorporation of such constraints is challenging in semiparametric models that allow latent variable distributions to be unknown, previous methods either constrain the median or avoid constraints. In ...
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We investigate the representation of hierarchical models in terms of marginals of other hierarchical models with smaller interactions. We focus on binary variables and marginals of pairwise interaction models whose hidden variables are conditionally independent given the visible variables. In this case the problem is equivalent to the representation of linear subspaces of polynomials by feedfor...
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ژورنال
عنوان ژورنال: Signal Processing
سال: 2004
ISSN: 0165-1684
DOI: 10.1016/j.sigpro.2003.10.014