Bayesian learning with multiple priors and nonvanishing ambiguity
نویسندگان
چکیده
منابع مشابه
Ambiguity, Measurability and Multiple Priors
The paper provides a notion of measurability which is suited for a class of Multiple Prior Models. Those characterized by nonatomic countably additive priors. Preferences generating such representations have been recently axiomatized in [12]. A notable feature of our definition of measurability is that an event is measurable if and only if it is unambiguous in the sense of Ghirardato, Maccheron...
متن کاملPAC-MDP Reinforcement Learning with Bayesian Priors
In an effort to build on recent advances in reinforcement learning and Bayesian modeling, this work (Asmuth et al., 2009) combines ideas from two lines of research on exploration in reinforcement learning or RL (Sutton & Barto, 1998). Bayesian RL research (Dearden et al., 1999; Poupart et al., 2006) formulates the RL problem as decision making in the belief space of all possible environment mod...
متن کاملLearning Bayesian priors for depth perception.
How the visual system learns the statistical regularities (e.g., symmetry) needed to interpret pictorial cues to depth is one of the outstanding questions in perceptual science. We test the hypothesis that the visual system can adapt its model of the statistics of planar figures for estimating three-dimensional surface orientation. In particular, we test whether subjects, when placed in an envi...
متن کاملBayesian generic priors for causal learning.
The article presents a Bayesian model of causal learning that incorporates generic priors--systematic assumptions about abstract properties of a system of cause-effect relations. The proposed generic priors for causal learning favor sparse and strong (SS) causes--causes that are few in number and high in their individual powers to produce or prevent effects. The SS power model couples these gen...
متن کاملNonparametric Bayesian Policy Priors for Reinforcement Learning
We consider reinforcement learning in partially observable domains where the agent can query an expert for demonstrations. Our nonparametric Bayesian approach combines model knowledge, inferred from expert information and independent exploration, with policy knowledge inferred from expert trajectories. We introduce priors that bias the agent towards models with both simple representations and s...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Economic Theory
سال: 2016
ISSN: 0938-2259,1432-0479
DOI: 10.1007/s00199-016-1007-y