Speaker: CHEN Zengjing
Time: 2020-11-04, 16:00
Place (virtual): Zoom ID 99854631775, Password 123456
Organizer: School of Mathematical Sciences
In this paper we introduce and study a Bernoulli-like model in the context of nonlinear probabilities, which we call the binary uncertainty model. This work is motivated mainly from the “two-armed bandit” problem. Our model provides a new way to study the “two-armed bandit” problem and, more generally, the distribution uncertainties. In one main result we obtain the central limit theorem for this model, and give an explicit formula for the limit distribution. The limit is shown to depend heavily on the structure of the events or the integrating functions, which demonstrate the key signature of nonlinear structure. We also establish the large deviation principle and, as an application, derive the weak law of large numbers. The large deviation rate function is identiﬁed explicitly. These limit theorems provide the theoretical foundation for statistical inferences.
According to the latest Nature Publishing Index (NPI) Asia-Pacific and The Nature Publishing Index China, University of Science and Technology of China tops in Chinese universities again. The rankings are based on the number of papers that were published in Nature journals during the last 12 months.
This article came from News Center of USTC.