Stochastic Complexity and Modeling

Statistical Curvature and Stochastic Complexity

We discuss the relationship between the statistical embedding curvature [1, 2] and the logarithmic regret [11] (regret for short) of the Bayesian prediction strategy (or coding strategy) for curved exponential families and Markov models. The regret of a strategy is defined as the difference of the logarithmic loss (code length) incurred by the strategy and that of the best strategy for each dat...

Stochastic Complexity and Newton Diagram

Many singular learning machines such as neural networks and mixture models are used in the information engineering field. In spite of their wide range applications, their mathematical foundation of analysis is not yet constructed because of the singularities in the parameter space. In recent years, we developed the algebraic geometrical method that shows the relation between the efficiency in B...

the effect of task complexity on lexical complexity and grammatical accuracy of efl learners’ argumentative writing

بر اساس فرضیه شناخت رابینسون (2001 و 2003 و 2005) و مدل ظرفیت توجه محدود اسکهان (1998)، این تحقیق تاثیر پیچیدگی تکلیف را بر پیچیدگی واژگان و صحت گرامری نوشتار مباحثه ای 60 نفر از دانشجویان زبان انگلیسی بررسی کرد. میزان پیچیدگی تکلیف از طریق فاکتورهای پراکندگی-منابع تعیین شد. همه ی شرکت کنندگان به صورت نیمه تصادفی به یکی از سه گروه: (1) گروه موضوع، (2) گروه موضوع + اندیشه و (3) گروه موضوع + اندی...

Stochastic Complexity and Its Applications

Stochastic Streams: Sample Complexity vs. Space Complexity

We address the trade-off between the computational resources needed to process a large data set and the number of samples available from the data set. Specifically, we consider the following abstraction: we receive a potentially infinite stream of IID samples from some unknown distribution D, and are tasked with computing some function f(D). If the stream is observed for time t, how much memory...