Maximum-Entropy Probability Distribution

References

(Stein et al., 2015) ⇒ Richard R. Stein, Debora S. Marks, and Chris Sander. (2015). “Inferring Pairwise Interactions from Biological Data Using Maximum-entropy Probability Models.” PLoS computational biology 11, no. 7
- QUOTE:
  Fig 3 - Scheme of pairwise maximum-entropy probability models.
  The maximum-entropy probability distribution with pairwise constraints for continuous random variables is the multivariate Gaussian distribution (left column). For the maximum-entropy probability distribution in the categorical variable case (right column), various approximative solutions exist, e.g., the mean-field, the sparse maximum-likelihood, and the pseudolikelihood maximization solution. The mean-field and the sparse maximum-likelihood result can be derived from the Gaussian approximation of binarized categorical variables (thin arrow). Pair scoring functions for the continuous case are the partial correlations (left column). For the categorical variable case, the direct information, the Frobenius norm, and the average product-corrected Frobenius norm are used to score pair couplings from the inferred parameters (right column).