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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

Algorithms

Time \(\text {complexity}^{\text{a}}\)

Space \(\text {complexity}^{\text{a}}\)

Marker effects

Missing phenotypes

BayesC\(\pi \)-I

\(O(npt_1)\)

NA

O(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