Hang Qian

Drawing on the sun, moon, dragon, phoenix, Nuwa, Yandi, Huangdi and other widely circulated cultural elements as examples, this book addresses the development and evolution of the most representative Chinese creation myths regarding nature, totems, ancestors and saints. The book not only interprets key creation myths, but also elaborates on the connection between the myths and some of the core values and concepts in Chinese civilization. For example, the long and jade culture is rooted in the Yellow Emperor’s revered jade weapon. Further, the book reveals the kernels of truth in the myths by presenting new research findings and research methods.

Drawing on the sun, moon, dragon, phoenix, Nuwa, Yandi, Huangdi and other widely circulated cultural elements as examples, this book addresses the development and evolution of the most representative Chinese creation myths regarding nature, totems, ancestors and saints. The book not only interprets key creation myths, but also elaborates on the connection between the myths and some of the core values and concepts in Chinese civilization. For example, the long and jade culture is rooted in the Yellow Emperor’s revered jade weapon. Further, the book reveals the kernels of truth in the myths by presenting new research findings and research methods.

This book delves into scalable Bayesian statistical methods designed to tackle the challenges posed by big data. It explores a variety of divide-and-conquer and subsampling techniques, seamlessly integrating these scalable methods into a broad spectrum of econometric models. In addition to its focus on big data, the book introduces novel concepts within traditional statistics, such as the summation, subtraction, and multiplication of conjugate distributions. These arithmetic operators conceptualize pseudo data in the conjugate prior, sufficient statistics that determine the likelihood, and the posterior as a balance between data and prior information, adding an intriguing dimension to Bayesian analysis. This book also offers a deep dive into Bayesian computation. Given the intricacies of floating-point representation of real numbers, computer programs can sometimes yield unexpected or theoretically impossible results. Drawing from his experience as a senior statistical software developer, the author shares valuable strategies for designing numerically stable algorithms. The book is an essential resource for a diverse audience: graduate students seeking foundational knowledge in Bayesian econometric models, early-career statisticians eager to explore cutting-edge advancements in scalable Bayesian methods, data analysts struggling with out-of-memory challenges in large datasets, and statistical software users and developers striving to program with efficiency and numerical stability.