000			02890 a2200265 4500
003			OSt
005			20220216110640.0
008			220214b xxu||||| |||| 00| 0 eng d
020			_a9781138492561
040			_cIIT Kanpur
041			_aeng
082			_a519.542 _bAl14p
100			_aAlbert, Jim
245			_aProbability and Bayesian modeling _cJim Albert and Jingchen Hu
260			_bCRC Press _c2020 _aBoca Raton
300			_axiv, 537p
440			_aChapman & HAll/CRC Texts in statistical science series
490			_a / edited by Joseph K. Blitzstein ...[et al.]
500			_aA Chapman & Hall Book
520			_aProbability and Bayesian Modeling is an introduction to probability and Bayesian thinking for undergraduate students with a calculus background. The first part of the book provides a broad view of probability including foundations, conditional probability, discrete and continuous distributions, and joint distributions. Statistical inference is presented completely from a Bayesian perspective. The text introduces inference and prediction for a single proportion and a single mean from Normal sampling. After fundamentals of Markov Chain Monte Carlo algorithms are introduced, Bayesian inference is described for hierarchical and regression models including logistic regression. The book presents several case studies motivated by some historical Bayesian studies and the authors’ research. This text reflects modern Bayesian statistical practice. Simulation is introduced in all the probability chapters and extensively used in the Bayesian material to simulate from the posterior and predictive distributions. One chapter describes the basic tenets of Metropolis and Gibbs sampling algorithms; however several chapters introduce the fundamentals of Bayesian inference for conjugate priors to deepen understanding. Strategies for constructing prior distributions are described in situations when one has substantial prior information and for cases where one has weak prior knowledge. One chapter introduces hierarchical Bayesian modeling as a practical way of combining data from different groups. There is an extensive discussion of Bayesian regression models including the construction of informative priors, inference about functions of the parameters of interest, prediction, and model selection. The text uses JAGS (Just Another Gibbs Sampler) as a general-purpose computational method for simulating from posterior distributions for a variety of Bayesian models. An R package ProbBayes is available containing all of the book datasets and special functions for illustrating concepts from the book. A complete solutions manual is available for instructors who adopt the book in the Additional Resources section.
650			_aProbabilities
650			_aBayesian statistical decision theory
700			_aHu, Jingchen
942			_cBK
999			_c565400 _d565400