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Table 2 Time and space complexity of alternative implementations of Bayesian regression models

From: Fast parallelized sampling of Bayesian regression models for whole-genome prediction

AlgorithmsTime \(\text {complexity}^{\text{a}}\)Space \(\text {complexity}^{\text{a}}\)
Marker effectsMissing phenotypes
BayesC\(\pi \)-I\(O(npt_1)\)NAO(np)
BayesC\(\pi \)-II\(O(p^2t_1)\)NA\(O(p^2)\)
BayesXII\(O(p^2t_2/k)\)\(O(p^2t_2/k)\)\(O(p^2)\)
  1. \(^{\text{a}}\)Variables include p, the number of markers; n, the number of observations; t1 and t2, the number of steps of MCMC required to converge in the BayesXII algorithm and conventional samplers for BayesC\(\pi \), respectively; k, the number of computer processors