Name: Bayesian Econometric Modelling for big Data (Chapman and Hall (en Inglés)
SKU: 64473679
Price: 104.13 USD
Availability: InStock
Author: Hang Qian

portada Bayesian Econometric Modelling for big Data (Chapman and Hall (en Inglés)

Bayesian Econometric Modelling for big Data (Chapman and Hall (en Inglés)

Formato

Libro Físico

Editorial

Crc Pr Inc

Autor

Hang Qian

Idioma

Inglés

N° páginas

466

Encuadernación

Tapa Dura

ISBN13

9781032915258

Bayesian Econometric Modelling for big Data (Chapman and Hall (en Inglés)

Hang Qian (Autor) · Crc Pr Inc · Tapa Dura

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Reseña del libro "Bayesian Econometric Modelling for big Data (Chapman and Hall (en Inglés)"

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.