When should we draw arrows? Assessing the use of Bayesian networks inphilosophy of science

Abramson, B. (1994). The design of belief network-based systems for price

forecasting. Computers & electrical engineering, 20(2), 163-180.

Achinstein, P. (1964). Models, analogies, and theories. Philosophy of Science,

(4), 328-350.

Andreassen, S., Woldbye, M., Falck, B., & Andersen, S. K. (1987). Munin: A causal probabilisticnetwork for interpretation of electromyographic findings. Proceedings of the 10th international joint conference on Artificial intelligence-

Volume 1, 366-372.

Beaumont, M. A., & Rannala, B. (2004). The bayesian revolution in genetics.

Nature Reviews Genetics, 5(4), 251-261.

Bondy, J., & Murty, U. (2008). Graph theory (Vol. 244). Springer New York.

Bovens, L., & Hartmann, S. (2004). Bayesian epistemology. Oxford University

Press.

Breese, J. S., Horvitz, E. J., Peot, M. A., Gay, R., & Quentin, G. H. (1992). Automated decisionanalytic diagnosis of thermal performance in gas turbines. Turbo Expo: Power for Land, Sea, and Air, 78972, V005T15A015.

Darwiche, A. (2009). Modeling and reasoning with bayesian networks. Cambridge University Press.

Dawid, R., Hartmann, S., & Sprenger, J. (2015). The no alternatives argument.

The British Journal for the Philosophy of Science, 66(1), 213-234.

DGL. (2022). Heterogeneous graphs. In Deep graph library (DGL) tutorials

and documentation. https://docs.dgl.ai/guide/graph-heterogeneous.html?

highlight=heterogenous

Dizadji-Bahmani, F., Frigg, R., & Hartmann, S. (2010). Who’s afraid of nagelian

reduction? Erkenntnis, 73(3), 393-412.

Dizadji-Bahmani, F., Frigg, R., & Hartmann, S. (2011). Confirmation and

reduction: A bayesian account. Synthese, 179(2), 321-338.

Gan, Q.,Wang, M., Li, M., Karypis, G., & Zhang, Z. (2021).Working with

heterogeneous graphs. In Deep graph library (dgl) tutorials and documentation.https://docs.dgl.ai/en/0.6.x/ tutorials/basics/5_hetero.html

Gu, Y., Peiris, D., Crawford, J.W., NcNicol, J., Marshall, B., & Jefferies, R.

A. (1994). An application of belief networks to future crop production.

Proceedings of the Tenth Conference on Artificial Intelligence for Applications,

-309.

Gutin, G. (2018). Acyclic digraphs. In J. Bang-Jensen & G. Gutin (Eds.),

Classes of directed graphs (pp. 125-172). Springer International Publishing.

https://doi.org/10.1007/978-3-319-71840-8_3

Hájek, A., & Hartmann, S. (2010). Bayesian epistemology. In J. Dancy (Ed.),

A companion to epistemology (pp. 93-105). Blackwell.

Harary, F., Norman, R. Z., & Cartwright, D. (1965). Structural models: Introduction to the theory of directed graphs. John Wiley & Sons Inc.

Hitchcock, C. (2020). Causal Models. In E. N. Zalta (Ed.), The Stanford encyclopedia of philosophy (Summer 2020). Metaphysics Research Lab, Stanford University.

Jarrom, D., Elston, L., Washington, J., Prettyjohns, M., Cann, K., Myles, S.,

& Groves, P. (2022). Effectiveness of tests to detect the presence of sars-cov-2 virus, and antibodies to sars-cov-2, to inform covid-19 diagnosis: A rapid systematic review. BMJ evidence-based medicine, 27(1), 33-45.

Kiiveri, H., Speed, T. P., & Carlin, J. B. (1984). Recursive causal models.

Journal of the australian Mathematical Society, 36(1), 30-52.

Kyrimi, E., McLachlan, S., Dube, K., Neves, M. R., Fahmi, A., & Fenton,

N. (2021). A comprehensive scoping review of bayesian networks in

healthcare: Past, present and future. Artificial Intelligence in Medicine, 117,

https://doi.org/10.1016/j.artmed.2021.102108

Lauritzen, S. (1979/1982). Lectures on contingency tables (2nd ed.). University

of Aalborg Press (Originally published in 1979).

Lauritzen, S. L., & Spiegelhalter, D. J. (1988). Local computations with probabilities on graphical structures and their application to expert systems.

Journal of the Royal Statistical Society: Series B (Methodological), 50 (2), 157-194.

Levitt, T. S., Agosta, J. M., & Binford, T. O. (1990). Model-based influence

diagrams for machine vision. In Machine intelligence and pattern recognition

(pp. 371-388, Vol. 10). Elsevier.

Nadi, F., Agogino, A. M., & Hodges, D. A. (1991). Use of influence diagrams

and neural networks in modeling semiconductor manufacturing

processes. IEEE Transactions on Semiconductor Manufacturing, 4(1), 52-58.

Nagel, E. (1961). The structure of science, problems in the logic of scientific

explanation.

Neapolitan, R. E. (2004). Learning bayesian networks. Pearson Prentice Hall.

Needham, C. J., Bradford, J. R., Bulpitt, A. J., & Westhead, D. R. (2007). A primer on learning in bayesian networks for computational biology. PLoS computational biology, 3(8), e129.

Pearl, J. (1985). Bayesian networks: A model of self-activated memory for

evidential reasoning. Proceedings of the 7th conference of the Cognitive Science Society, University of California, Irvine, CA, USA, 329-334.

Pearl, J. (1988). Probabilistic reasoning in intelligent systems: Networks of plausible inference.Morgan Kaufmann.

Pearl, J. (2009). Causality. Cambridge University Press.

Rehg, J. M., Murphy, K. P., & Fieguth, P. W. (1999). Vision-based speaker

detection usingbayesian networks. Proceedings. 1999 IEEE Computer Society

Conference on Computer Vision and Pattern Recognition (Cat. No PR00149),

, 110-116.

Sarkar, S. (2015). Nagel on reduction. Studies in History and Philosophy of Science Part A, 53, 43-56.

Schaffner, K. F. (1967). Approaches to reduction. Philosophy of science, 34(2),

-147.

Spiegelhalter, D. J., Dawid, A. P., Lauritzen, S. L., & Cowell, R. G. (1993).

Bayesian analysisin expert systems. Statistical science, 219-247.

Sprenger, J. (2016). The probabilistic no miracles argument. European Journal

for Philosophy of Science, 6(2), 173-189.

Sprenger, J., & Hartmann, S. (2019). Bayesian philosophy of science. Oxford

University Press.

Talbott, W. (2016). Bayesian Epistemology. In E. N. Zalta (Ed.), The Stanford

encyclopedia of philosophy (Winter 2016). Metaphysics Research Lab, Stanford

University.

Tesic, M. (2019). Confirmation and the generalized Nagel-Schaffner model

of reduction: A Bayesian analysis. Synthese, 196(3), 1097-1129.

Van Riel, R. (2014). The concept of reduction. Springer.

Wang, X., Ji, H., Shi, C., Wang, B., Ye, Y., Cui, P., & Yu, P. S. (2019). Heterogeneous

graph attention network. The World Wide Web Conference,

-2032. https://doi.org/10.1145/3308558.3313562

Wermuth, N., & Lauritzen, S. L. (1982). Graphical and recursive models for contigency tables. Institut for Elektroniske Systemer, Aalborg Universitetscenter.

Wiegerinck, W., Burgers, W., & Kappen, B. (2013). Bayesian networks, introduction and practical applications. In M. Bianchini, M. Maggini, & L. C.

Jain (Eds.), Handbook on neural information processing (pp. 401-431). Springer

Berlin Heidelberg. https://doi.org/10.1007/978-3-642-36657-4_12

Wimsatt, W. C. (1974). Reductive explanation: A functional account. PSA:

Proceedings of the biennial meeting of the Philosophy of Science Association,

, 671-710.

Wright, S. (1921). Correlation and causation. Journal of Agricultural Research,

(20), 557-585.