This is the second part of a special feature which focuses on probabilistic graphical modeling approaches to biomedical informatics. Four invited papers were published in Part 1 (He et al. 2017; Liu et al. 2017; Taguri and Izumi 2017; Yamamoto et al. 2017). Part 2 follows this article, with two contributed papers. The first one is “Error asymmetry in causal and anticausal regression” by Patrick Blöbaum, Takashi Washio, and Shohei Shimizu. The second article is “A note on large-scale logistic prediction. Using an approximate graphical model to deal with collinearity and missing data” by Maarten Marsman, Lourens Waldorp, Gunter Maris. We appreciate their contributions, as well as those of the anonymous reviewers.
References
He Y, Jia J, Geng Z (2017) Structural learning of causal networks. Behaviormetrika 44(1):287–305
Liu S, Fukumizu K, Suzuki T (2017) Learning sparse structural changes in high-dimensional Markov networks. Behaviormetrika 44(1):265–286
Taguri M, Izumi S (2017) A global goodness-of-fit test for linear structural mean models. Behaviormetrika 44(1):253–262
Yamamoto M, Hirose K, Nagata H (2017) Graphical tool of sparse factor analysis. Behaviormetrika 44(1):229–250
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Suzuki, J., Malone, B. Special feature: probabilistic graphical models and its applications to biomedical informatics—part 2. Behaviormetrika 44, 489 (2017). https://doi.org/10.1007/s41237-017-0025-9
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DOI: https://doi.org/10.1007/s41237-017-0025-9