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It is a pleasure to congratulate Ni et al. (Stat Methods Appl 490:1–32, 2021) on the recent advances in Bayesian graphical models reviewed in Ni et al. (Stat Methods Appl 490:1–32, 2021). The authors have given considerable thought to the construction and estimation of Bayesian graphical models that capture salient features of biological networks. My discussion focuses on computational challenges and opportunities along with priors, pointing out limitations of the Markov random field priors reviewed in Ni et al. (Stat Methods Appl 490:1–32, 2021) and exploring possible generalizations that capture additional features of conditional independence graphs, such as hub structure and clustering. I conclude with a short discussion of the intersection of graphical models and random graph models.
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How do you assert a graph? Towards an account of depictions in scientific testimony
Abstract I extend the literature on norms of assertion to the ubiquitous use of graphs in scientific papers and presentations, which I term “graphical testimony.” On my account, the testimonial presentation of a graph involves commitment to both (a) the in‐context reliability of the graph's framing devices and (b) the perspective‐relative accuracy of the graph's content. Despite apparent disagreements between my account and traditional accounts of assertion, the two are compatible and I argue that we should expect a similar pattern of commitments in a set of cases that extends beyond the graphical one. I end by demonstrating that the account resolves apparent tensions between the demands of honesty and the common scientific practice of presenting idealized or simplified graphs: these “distortions” can be honest so long as there's the right kind of alignment between the distortion and the background beliefs and values of the audience.
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- Award ID(s):
- 2042366
- PAR ID:
- 10618627
- Publisher / Repository:
- Wiley
- Date Published:
- Journal Name:
- Noûs
- ISSN:
- 0029-4624
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1111/nous.12529
