Bayesian advocates of expected utility maximization use sets of probability distributions to represent very different ideas. Strict Bayesians insist that probability judgment is n...
Nonparametric Bayesian models are often based on the assumption that the objects being modeled are exchangeable. While appropriate in some applications (e.g., bag-ofwords models f...
Kurt T. Miller, Thomas L. Griffiths, Michael I. Jo...
We propose a new model of human concept learning that provides a rational analysis for learning of feature-based concepts. This model is built upon Bayesian inference for a gramma...
Noah D. Goodman, Joshua B. Tenenbaum, Jacob Feldma...
In this paper we present techniques by which each mix in a mix network can be paid for its services by message senders, in a way that ensures fairness and without sacrificing anon...
In a rational programming language, a program specifies a situation faced by an agent; evaluating the program amounts to computing what a rational agent would believe or do in the...