Probabilistic Graphical Models (PGMs) offer a robust yet intuitive framework to deal with uncertainty and complexity and have been effectively applied to diverse problems across multiple domains. While the majority of work has focused on cross-sectional data, there has been a recent increase of...

We use a reinforcement model to compute the hedging policy for Credit Valuation Adjustment ( CVA ) problems. Reinforcement learning can be used to solve financial applications ofintertemporal choice. In finance, common problems of this kind include pricing and hedging ofcontingent claims,...

This article describes a tutorial for learning to use Tableau Public, which is free, powerful, and widely used software for data visualization. Data literacy is an important component of undergraduate research projects. This paper introduces an exercise that teaches the fundamental Tableau...

We study a canonical setting of learning in networks where initially agents receive conditionally i.i.d. signals about a binary state. The distribution according to which signals are drawn is called an information structure. Agents repeatedly communicate beliefs with their neighbors and update...

This study aims to determine whether the application of brain-friendly learning through whole brain teaching gives a positive effect on the creative character of students, to know the response of the students against the application of brain-friendly learning through whole brain teaching, and to...

This study aimed to determine the performance of Grade 5 pupils in Lower Bonga Elementary School (LBES) in learning the multiplication of one to two-digit whole numbers using a magic finger technique. Specifically, the following questions were formulated: (1) What is the performance of pupils in...

This paper addresses the steep learning curve in Machine Learning faced by noncomputer scientists, particularly social scientists, stemming from the absence of a primer on its fundamental principles. I adopt a pedagogical strategy inspired by the adage "once you understand OLS, you can work your...

This paper addresses the steep learning curve in Machine Learning faced by non-computer scientists, particularly social scientists, stemming from the absence of a primer on its fundamental principles. I adopt a pedagogical strategy inspired by the adage ”once you understand OLS, you can work...

This paper is prepared to provide a brief introduction to the topic of Ensemble Learning. It aims to provide the reader with a broad overview on the approach of Ensemble Methods.Sections:-What is Ensemble Learning?-The Rationale Behind Ensemble Methods-Common Approaches To Ensemble...

In dynamic promotion contests, where performance measurement is noisy and ordinal, selection can be improved by biasing later stages in favor of early leaders. Even in the worst-case scenario, where noise swamps ability differences in determining relative performance, optimal bias is i) strictly...