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2020 – today
- 2023
[j23]Jürgen Landes, Soroush Rafiee Rad, Jon Williamson:
Determining Maximal Entropy Functions for Objective Bayesian Inductive Logic. J. Philos. Log. 52(2): 555-608 (2023)- 2022
- [j22]Jürgen Landes, Jon Williamson:
Objective Bayesian Nets for Integrating Consistent Datasets. J. Artif. Intell. Res. 74: 393-458 (2022) - 2021
- [j21]Jürgen Landes, Soroush Rafiee Rad, Jon Williamson:
Towards the entropy-limit conjecture. Ann. Pure Appl. Log. 172(2): 102870 (2021) - [j20]Jon Williamson
:
Establishing the teratogenicity of Zika and evaluating causal criteria. Synth. 198(10-S): 2505-2518 (2021) - [j19]Jon Williamson:
Correction to: Establishing the teratogenicity of Zika and evaluating causal criteria. Synth. 198(10-S): 2519 (2021)
2010 – 2019
- 2018
- [j18]Jan-Willem Romeijn, Jon Williamson:
Intervention and Identifiability in Latent Variable Modelling. Minds Mach. 28(2): 243-264 (2018) - 2016
- [j17]Jürgen Landes, Jon Williamson:
Special issue: Combining probability and logic. J. Appl. Log. 14: 1-2 (2016) - 2015
- [j16]Jürgen Landes, Jon Williamson:
Justifying Objective Bayesianism on Predicate Languages. Entropy 17(4): 2459-2543 (2015) - [j15]Jürgen Landes, Jon Williamson:
Maximum Entropy Applied to Inductive Logic and Reasoning. Entropy 17(5): 3458-3460 (2015) - 2014
- [j14]Brendan Clarke, Bert Leuridan, Jon Williamson:
Modelling mechanisms with causal cycles. Synth. 191(8): 1651-1681 (2014) - 2013
- [j13]Jürgen Landes, Jon Williamson:
Objective Bayesianism and the Maximum Entropy Principle. Entropy 15(9): 3528-3591 (2013) - [j12]Jon Williamson:
From Bayesian epistemology to inductive logic. J. Appl. Log. 11(4): 468-486 (2013) - 2011
- [j11]Jon Williamson:
Objective Bayesianism, Bayesian conditionalisation and voluntarism. Synth. 178(1): 67-85 (2011) - 2010
- [c2]Lorenzo Casini, Phyllis McKay Illari, Federica Russo, Jon Williamson:
Recursive Bayesian Nets for Prediction, Explanation and Control in Cancer Science - A Position Paper. BIOINFORMATICS 2010: 233-238 - [p3]Jon Williamson:
The Philosophy of Science and its relation to Machine Learning. Scientific Data Mining and Knowledge Discovery 2010: 77-89
2000 – 2009
- 2009
- [j10]Fábio Gagliardi Cozman, Rolf Haenni, Jan-Willem Romeijn, Federica Russo, Gregory R. Wheeler, Jon Williamson:
Combining Probability and Logic. J. Appl. Log. 7(2): 131-135 (2009) - [j9]Jon Williamson:
Aggregating Judgements by Merging Evidence. J. Log. Comput. 19(3): 461-473 (2009) - 2008
- [j8]Jon Williamson:
Objective Bayesian probabilistic logic. J. Algorithms 63(4): 167-183 (2008) - [j7]Jon Williamson:
Objective Bayesianism with predicate languages. Synth. 163(3): 341-356 (2008) - [p2]Rolf Haenni, Jan-Willem Romeijn, Gregory R. Wheeler, Jon Williamson:
Possible Semantics for a Common Framework of Probabilistic Logics. Interval / Probabilistic Uncertainty and Non-Classical Logics 2008: 268-279 - [p1]Sylvia B. Nagl, Matthew Williams, Jon Williamson:
Objective Bayesian Nets for Systems Modelling and Prognosis in Breast Cancer. Innovations in Bayesian Networks 2008: 131-167 - 2006
- [j6]Jon Williamson:
Introduction. J. Log. Lang. Inf. 15(1-2): 1-3 (2006) - [j5]Matthew Williams, Jon Williamson:
Combining Argumentation and Bayesian Nets for Breast Cancer Prognosis. J. Log. Lang. Inf. 15(1-2): 155-178 (2006) - [j4]Jon Williamson:
Dispositional versus epistemic causality. Minds Mach. 16(3): 259-276 (2006) - 2005
- [c1]Jon Williamson:
Objective Bayesian Nets. We Will Show Them! (2) 2005: 713-730 - 2004
- [j3]Jon Williamson:
A Dynamic Interaction Between Machine Learning and the Philosophy of Science. Minds Mach. 14(4): 539-549 (2004) - 2003
- [j2]Jon Williamson, Dov M. Gabbay:
Special issue on Combining Probability and Logic. J. Appl. Log. 1(3-4): 135-138 (2003) - [j1]Jon Williamson:
Bayesianism and Language Change. J. Log. Lang. Inf. 12(1): 53-97 (2003)
Coauthor Index
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