There are two main types: directed graphical models (or Bayesian networks) and undirected graphical models (or Markov networks).
Recent papers based on Gaussian graphical models or Bayesian networks [ 42, 43, 70- 72] take into account all the observed variables of a dataset to infer direct correlation or directional correlation.
Hence, variance shrinkage (as occurs in the hierarchical Bayesian regression models) or Bayesian regularization (as occurs in BRANN) plays a crucial role in attenuating "over-fitting" and attaining reproducible predictive performance.
One type of approaches is based on genomic data, such as histone modification (ChIP-Seq) or chromatin components (DamID), to segment the whole genome into elaborate organizational units (called as states or domains) with the computational frameworks such as Hidden Markov Models or Bayesian networks [ 6- 8].
Related methods Mixtures of Dependence Trees are part of the graphical model (or Bayesian network) formalism (Friedman, 2004).
Different methods have been developed to integrate the results from various microarray studies, though most of them rely on distributional assumptions, such as the t-statistic based, mixed-effects model, or Bayesian model methods.
It is possible to consider strategies that use mixed model or Bayesian approaches to predict weaning weight means for lambs in the reference classes in individual management groups as frequency-dependent combinations of overall, flock, and management group means.
Indeed, quantitative simulation studies can benefit from a variety of quantitative uncertainty assessments, including sensitivity analysis, error propagation equations, inverse modelling, scenario analysis, Monte Carlo simulation or Bayesian statistical modelling (Refsgaard et al. 2007).
In terms of model selection or Bayesian model averaging for the i-th response gene, the 2 m possible values of the m-tuple (α i 1, α i 2,…, α im ) span the sub-model space.
Our example analyses suggest that the use of the extra information present in an ordered categorical or censored Gaussian data set, instead of dichotomizing the data into case-control observations, increases the accuracy of genomic breeding values predicted by Bayesian multilocus association models or by Bayesian genomic best linear unbiased prediction.
Our example analyses show that using the extra classes and the uncensored observations present in an ordered categorical and a censored Gaussian data set, instead of dichotomizing the data into case-control observations, increases the accuracy of genomic breeding values predicted by Bayesian multilocus association models or by Bayesian G-BLUP.
