Sure enough: efficient Bayesian learning and choice
نویسندگان
چکیده
منابع مشابه
Sequential Choice and Non-Bayesian Observational Learning
Models of observational learning in settings of sequential choice have two key features. The first is that players make decisions by using Bayes’ rule to update their beliefs about payoffs from a common prior. The second is that each agent’s decision rule is common knowledge, so that subsequent players can draw inferences about unobserved private signals from observable actions. In this paper, ...
متن کاملBayesian Efficient Multiple Kernel Learning
Multiple kernel learning algorithms are proposed to combine kernels in order to obtain a better similarity measure or to integrate feature representations coming from different data sources. Most of the previous research on such methods is focused on the computational efficiency issue. However, it is still not feasible to combine many kernels using existing Bayesian approaches due to their high...
متن کاملEfficient Bayesian Clustering for Reinforcement Learning
A fundamental artificial intelligence challenge is how to design agents that intelligently trade off exploration and exploitation while quickly learning about an unknown environment. However, in order to learn quickly, we must somehow generalize experience across states. One promising approach is to use Bayesian methods to simultaneously cluster dynamics and control exploration; unfortunately, ...
متن کاملBayesian Learning for Efficient Visual Inference
An interesting subset of problems in the field of computer vision require the inference of a continuous valued quantity from image data. This dissertation describes the visual inference machine (VIM), a general method for learning the mapping from image data to a continuous output space using the Bayesian rules of inference. The learning is performed without needing to define a generative model...
متن کاملEfficient Reinforcement Learning with Bayesian Optimization
OF THE DISSERTATION Efficient Reinforcement Learning with Bayesian Optimization By Danyan Ganjali Doctor of Philosophy in Mechanical and Aerospace Engineering University of California, Irvine, 2016 Professor Athanasios Sideris, Chair A probabilistic reinforcement learning algorithm is presented for finding control policies in continuous state and action spaces without a prior knowledge of the d...
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: Animal Cognition
سال: 2017
ISSN: 1435-9448,1435-9456
DOI: 10.1007/s10071-017-1107-5