(1) Why did Truman, who lived in most ways quite modest life financially speaking after his presidency, lobby so furiously for pension benefits that he by no stretch of the imagination needed, given that he managed to use the presidency to become very rich?. But, according to Campos, everyone always said that Truman was broke, so I guess my dad was just repeating what he’d read in the newspapers.Ĭampos asks two questions, and I want to address both of them here. When I saw all this, I was surprised because I remember my dad telling me how Truman was broke after he left the White House, unlike corrupt Richard Nixon, who made money during his time in office. I don’t know anything about this, but the article has a lot of detail and it seems convincing, so I’ll go with it until informed otherwise. I read this post by Paul Campos that says that Harry Truman made tons of money as president and then he got even richer afterward. (This post is by Aki, one of the many co-authors of the paper) Posted in Bayesian Statistics | 1 Reply $ This allows recognizing under-studied directions in prior elicitation research, finally leading to a proposal of several new avenues to improve prior elicitation methodology. The existing prior elicitation literature is reviewed and categorized in these terms. Why are we not widely using prior elicitation? We analyze the state of the art by identifying a range of key aspects of prior knowledge elicitation, from properties of the modelling task and the nature of the priors to the form of interaction with the expert. We even lack a comprehensive theoretical framework for understanding different facets of the prior elicitation problem. We lack elicitation methods that integrate well into the Bayesian workflow and perform elicitation efficiently in terms of costs of time and effort.
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In practice, however, we are still fairly far from having usable prior elicitation tools that could significantly influence the way we build probabilistic models in academia and industry. Prior elicitation transforms domain knowledge of various kinds into well-defined prior distributions, and offers a solution to the prior specification problem, in principle. Specification of the prior distribution for a Bayesian model is a central part of the Bayesian workflow for data analysis, but it is often difficult even for statistical experts.
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Martin, Suyog Chandramouli, Marcelo Hartmann, Oriol Abril Pla, Owen Thomas, Henri Pesonen, Jukka Corander, Aki Vehtari, Samuel Kaski, Paul-Christian Bürkner, and Arto Klami write