McCandless, Gustafson and Austin (2009) describe a Bayesian approach to regression adjustment for the propensity score to reduce confounding. A unique property of the method is that the treatment and outcome models are combined via Bayes theorem. However, this estimation procedure can be problematic if the outcome model is misspecified. We observe feedback that can bias propensity score estimat...

In earlier work, we introduced exible inference and decision-theoretic metareasoning to address the intractability of normative inference. Here, rather than pursuing the task of computing beliefs and actions with decision models composed of distinctions about uncertain events, we examine methods for inferring beliefs about mathematical truth before an automated theorem prover completes a proof....

Performance-based regulation (PBR) is influenced by the Bayesian and non-Bayesian incentive mechanisms. While Bayesian incentives are impractical, the insights from their properties can be combined with practical non-Bayesian mechanisms for application to transmission pricing. This combination suggests an approach based on the distinction between ultra-short, short and long periods. Ultra-short...

We describe scoring metrics for learning Bayesian networks from a combination of user knowledge and statistical data. Previ­ ous work has concentrated on metrics for do­ mains containing only discrete variables, un­ der the assumption that data represents a multinomial sample. In this paper, we ex­ tend this work, developing scoring metrics for domains containing only continuous variables under...

2590 Suppose one person, call him Sender, wishes to persuade another, call her Receiver, to change her action. If Receiver is a rational Bayesian, can Sender persuade her to take an action he would prefer over the action she was originally going to take? If Receiver understands that Sender chose what information to convey with the intent of manipulating her action for his own benefit, can Sende...

In earlier work, we introduced flexible infer­ ence and decision-theoretic metareasoning to address the intractability of normative infer­ ence. Here, rather than pursuing the task of computing beliefs and actions with decision models composed of distinctions about uncer­ tain events, we examine methods for inferring beliefs about mathematical truth before an automated theorem prover completes ...

Performance-based regulation (PBR) is influenced by the Bayesian and non-Bayesian incentive mechanisms. While Bayesian incentives are impractical, the insights from their properties can be combined with practical non-Bayesian mechanisms for application to transmission pricing. This combination suggests an approach based on the distinction between ultra-short, short and long periods. Ultra-short...

Bayesian theory [5] is one of the most commonly utilized theories for managing uncertainty in information fusion [20, 12]. The theory relies on two main assumptions: (1) a probability function should be used for representing belief and (2) Bayes’ theorem should be used for belief updating when a new observation has been made. The main criticism of Bayesian theory that can be found in the litera...

ion, 49 Adams, Cecil, 23 Adult Intelligence Scale, 44, 118 adult weight, 47, 48, 114 analysis, 49, 74, 88 anecdotal evidence, 2, 9 Anscombe’s quartet, 113 apparent effect, 8, 10, 20, 79 artifact, 8, 10 Australia, 37 average, 11, 68 bar plot, 15, 18 baseball, 61 basketball, 61 Bayes factor, 86 Bayes’s theorem, 63 Bayesian estimation, 77, 97, 104 Bayesian probability, 84, 86 Bayesianism, 54, 66, ...