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,...

We propose a general framework for studying the evolution ofheterogeneous beliefs in a dynamic feedback setting. Beliefsdistributions are defined on a continuous space representingthe possible strategies agents can choose from. Agents base theirchoices on past performances. As new information...

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...

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 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...

For treatment effects - one of the core issues in modern econometric analysis - prediction and estimation are flip-sides of the same coin. As it turns out, machine learning methods are the tool for generalized prediction models. Combined with econometric theory allows us to estimate not only the...

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...

We show how to bound the effect of belief-dependent preferences on choices in sequential two-player games without information about the (higher-order) beliefs of players. The approach can be applied to a class of belief-dependent preferences which includes reciprocity (Dufwenberg and...

Convolutional neural networks (CNN) and long short-term memory (LSTM) networks have become a staple of sequence learning. Due to the well-established fact that financial time series data exhibit exceptionally noisy characteristics, capital market anomalies are virtually impossible to detect. We...