Scientific Reasoning: The Bayesian Approach

By C. Howson, P. Urbach. 1989

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Citations in the corpus

Short-term plasticity as cause–effect hypothesis testing in distal reward learning (2014, methodology): “Building on models such as Izhikevich (2007); Florian (2007); Friedrich et al (2011); Soltoggio and Steil (2013), the current model introduces the concept of testing hypotheses with ambiguous information flow.”
Because Hitler did it! Quantitative tests of Bayesian argumentation using ad hominem (2012, background): “That is, propositions do not have truth values of 0 or 1, but rather an individual’s degree of belief in a particular proposition can be understood as a probability between 0 and 1 (see also e.g., Evans & Over, 2004; Howson & Urbach, 1996; Oaksford & Chater, 1998, 2007).”
Bayesian Estimation and Inference (2015, methodology): “However, quantitative researchers in the social and organizational sciences have historically relied on alternative estimation and inference methods, such as maximum likelihood, p values, confidence intervals, and NHST, which all have non-Bayesian origins (e.g., Fisher, 1922, 1925, 1935; Neyman, 193”
Bayesian Methods in Conservation Biology (2000, methodology): “Statistical methods based on Bayes’s theorem (Bayes 1763) represent a different school of statistical inference and a different statistical philosophy from the standard statistics that most scientists are taught ( Jeffreys 1939, 1961; Berger 1985; Howson & Urbach 1989, 1991; Lee 1989; Press 1989).”
You May Believe You Are a Bayesian But You Are Probably Wrong (2011, background): “My first concerns an example of Howson and Urbach (1989).”
Philosophy and the practice of Bayesian statistics (2010, unknown): “These views differ from those of, e.g., Bernardo and Smith (1994) or Howson and Urbach (1989) only in the omission of technical details.”
Resurrecting Logical Probability (2001, background): “…assignments necessarily exhibit a more or less strong bias against certain types of world, which we have no right, in advance of all empirical information, to indulge in. (Howson and Urbach 1993, p. 69) That is a just criticism of Carnap, but is it not a case of the pot calling the kettle black?”
The role of auxiliary assumptions for the validity of manipulations and measures (2012, background): “…the strong inference version of falsification (Platt, 1964), verification (Hempel, 1965), Bayesian evaluation (Edwards, Lindman, & Savage, 1963; Howson & Urbach, 1989; see Trafimow, 2003, 2005, 2006 for recent discussions), or others, it seems clear that the goal of basic researchers is to be…”