The algorithm fits a mixture of a beta(1,beta) and beta(alpha,1) distribution to observed marginal regulator activities. Based on this mixture a cutoff is chosen such that the expected false positive rate is below a defined threshold.

1 | ```
suggestThreshold(prob, fpr=0.001)
``` |

`prob` |
marginal probability obtained from |

`fpr` |
threshold for accepted false positive rate |

a cutoff for marginal activity probabilities

Holger Froehlich

Froehlich, H. and Klau, G. (2013). Reconstructing Consensus Bayesian Network Structures with Application to Learning Molecular Interaction Networks. In: Beissbarth, T., Kollmar, M., Leha, A., Morgenstern, B., Schultz, A.-K., Waack, S., and Wingender, E., editors, Proc. German Conference on Bioinformatics, Open Access Series in Informatics, pages 46 - 55. Schloss Dagstuhl - Leibniz-Zentrum fuer Informatik, Dagstuhl Publishing, Germany.

1 2 | ```
freq = 0.2*rbeta(100, 1, 10) + 0.8*rbeta(100, 5, 1)
thresh = suggestThreshold(freq)
``` |

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