000			02827cam a22003618i 4500
001			24144208
003			OSt
005			20251231124952.0
008			250408s2025 flu b 001 0 eng
010			_a 2024057171
020			_a9781032915258 (hbk.)
035			_a24144208
040			_aDLC _beng _erda _cJKRC
082	0	0	_a519.542 _223 _bQIA.H
100	1		_aQian, Hang. _eauthor. _938213
245	1	0	_aBayesian econometric modelling for big data / _cby Hang Qian. _hEnglish.
250			_a1st.
260			_aBoca Raton, FL : _bCRC Press Taylor & Francis Group, _c2025.
300			_axix, 466 p. ; _c26 cm.
336			_atext _btxt _2rdacontent
337			_aunmediated _bn _2rdamedia
338			_avolume _bnc _2rdacarrier
365			_b96.99 _cPound
490	0		_aChapman and Hall/CRC Series on Statistics in Business and Economics
504			_aIncludes bibliographical references and index.
520			_a"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"-- _cProvided by publisher.
650		0	_aBayesian statistical decision theory. _936784
650		0	_aBig data.
776	0	8	_iOnline version: _aQian, Hang. _tBayesian econometric modelling for big data _dBoca Raton, FL : CRC Press, 2025 _z9781003564027 _w(DLC) 2024057172
906			_a7 _bcbc _corignew _d1 _eecip _f20 _gy-gencatlg
942			_2ddc _c1 _e23 _n0
999			_c613166 _d613166