Open Daily: 10am - 10pm | Alley door pickup 10am - 7pm 3038 Hennepin Ave Minneapolis, MN
612-822-4611

  • Open Daily: 10am - 10pm
    Alley-side Pickup: 10am - 7pm

    3038 Hennepin Ave Minneapolis, MN
    612-822-4611

Open Daily: 10am - 10pm | Alley-side Pickup: 10am - 7pm
3038 Hennepin Ave Minneapolis, MN
612-822-4611
Computational Bayesian Statistics

Computational Bayesian Statistics

Amaral Turkman, M. Antónia
Müller, Peter
Paulino, Carlos Daniel

Hardcover

Series: Institute of Mathematical Statistics Textbooks, Book 11

General ComputersProbability & Statistics

ISBN10: 1108481035
ISBN13: 9781108481038
Publisher: Cambridge University Press
Published: Feb 28 2019
Pages: 254
Weight: 1.00
Height: 0.70 Width: 9.10 Depth: 9.30
Language: English
Meaningful use of advanced Bayesian methods requires a good understanding of the fundamentals. This engaging book explains the ideas that underpin the construction and analysis of Bayesian models, with particular focus on computational methods and schemes. The unique features of the text are the extensive discussion of available software packages combined with a brief but complete and mathematically rigorous introduction to Bayesian inference. The text introduces Monte Carlo methods, Markov chain Monte Carlo methods, and Bayesian software, with additional material on model validation and comparison, transdimensional MCMC, and conditionally Gaussian models. The inclusion of problems makes the book suitable as a textbook for a first graduate-level course in Bayesian computation with a focus on Monte Carlo methods. The extensive discussion of Bayesian software - R/R-INLA, OpenBUGS, JAGS, STAN, and BayesX - makes it useful also for researchers and graduate students from beyond statistics.

1 different editions

Also available

Also in

General Computers

View All