Sinposis

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.