همگرایی دنباله ها (i.i.d)

دنباله ها و شرایط معکوس‌پذیری عملگر ضربی سازs-g-

نظریه قاب یکی از موضوعات تحقیقاتی ریاضی است که در دهه‌های اخیر در حل مسائل مختلف کاربردی و شاخه‌های مرتبط با ریاضیات به عنوان یک ابزار دقیق و کارآمد مورد استفاده قرار گرفته است. در این راستا، مطالعه عملگر‏ ضربی‌ساز که نقش بسزایی در موارد فوق دارد‏، اهمیت یافته و در این صفحات مورد هدف می‌باشد. این مقاله به ارائه‌ی شرایط کافی برای معکوس‌پذیری عملگر ضربی‌ساز متناظر با $‎s‎$‎‎‏-‎$‎‎‎g‎$‎‎‎-قاب‌ها، ...

Limiting Curves for IID Records

We consider the concentration of measure for n iid, two-dimensional random variables, under the conditioning that they form a record. Under mild conditions, we show that all random variables tend to concentrate, as n ! 1, around limiting curves, which are the solutions of an appropriate variational problem. We also show that the same phenomenum occurs, without the records conditioning, for the ...

IID: independently and indistinguishably distributed

The inability of the Bayesian model to accommodate Ellsberg-type behavior is well-known. This paper focuses on another limitation of the Bayesian model, specific to a dynamic setting, namely the inability to permit a distinction between experiments that are identical and those that are only indistinguishable. It is shown that such a distinction is afforded by recursive multiple-priors utility. ...

Factors of IID on Trees

Classical ergodic theory for integer-group actions uses entropy as a complete invariant for isomorphism of IID (independent, identically distributed) processes (a.k.a. product measures). This theory holds for amenable groups as well. Despite recent spectacular progress of Bowen, the situation for non-amenable groups, including free groups, is still largely mysterious. We present some illustrati...

مجموعه دست‌آورد دنباله همساز و دنباله معکوس اعداد فیبوناتچی

Coupled Matrix Factorization Within Non-IID Context

Recommender systems research has experienced different stages such as from user preference understanding to content analysis. Typical recommendation algorithms were built on the following bases: (1) assuming users and items are IID, namely independent and identically distributed, and (2) focusing on specific aspects such as user preferences or contents. In reality, complex recommendation tasks ...