Lectures on Probability and Statistics for Graduate-Level Economics

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【簡介】 Sound knowledge of rigorous probability and statistics methods is essential to pursue graduate studies in economics. These sorts of tools are largely required to conduct research in modern fields of economics such as economic theory, empirical economics, experimental economics, or data science for economics. These notes provide an intuitive roadmap to navigate graduate-level courses in mathematical probability and statistics for economists. Each chapter presents questions prevalent on each topic and lays out the state-of-the-art theoretical frameworks used to address such questions. The book offers a diverse array of solved examples to help gain intuitions on abstract concepts, as well as unsolved exercises to stimulate the readers’ training in such concepts. The book presents the theoretical side of probability and statistics in a rather concise way and stresses the importance of motivating examples and observations. 【目錄】

PROBABILITY: A LIVELY INTRODUCTION

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【簡介】 Probability has applications in many areas of modern science, not to mention in our daily life. Its importance as a mathematical discipline cannot be overrated, and it is a fascinating and surprising topic in its own right. This engaging textbook with its easy-to-follow writing style provides a comprehensive, yet concise introduction to the subject. It covers all of the standard material for undergraduate and first-year-graduate-level courses as well as many topics that are usually not found in standard text - such as Bayesian inference, Markov chain Monte Carlo simulation, and Chernoff bounds. Stresses why probability is so relevant and how to apply it - students won't simply learn probability, they will understand it Perfectly balances theory and applications - the fundamental concepts are explained with engaging real-world examples and many problem-solving tips are provided 【目錄】 1. Foundations of probability theory 2. Conditional probability 3. Discrete random variables 4. Continuous random variables 5. Jointly distributed random variables 6. Multivariate normal distribution 7. Conditioning by random variables 8. Generating functions 9. Additional topics in probability 10. Discrete-time Markov chains 11. Continuous-time Markov chains