Abstract
The aim of this chapter is to familiarize a general reader with some important statistical techniques that are used in subsequent chapters. The chapter contains four distinct sections. The first section deals with the basic concepts of probability and random variables. The second section is devoted to statistical inference which explains concepts such as the sampling distribution, point estimator, and confidence interval. The third section discusses Bayesian statistics, beginning with Bayes’ theorem and concluding with Bayesian inference. The last section serves as an introduction to random fields, image restoration within a Bayesian paradigm, and some associated techniques such as stochastic relaxation.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Miller I, Miller M (1999) John E Freund’s mathematical statistics with applications. Prentice Hall, New York
Bhattacharyya GK, Johnson RA (1977) Statistical concepts and methods. Wiley, New York
Gelman A, Carlin JB, Stern HS, Rubin DB (2000) Bayesian data analysis. Chapman & Hall/CRC, London
Li SZ (1995) Markov random field modeling in computer vision. Springer, London
Geman S, Geman D (1984) Stochastic relaxation, Gibbs distributions, and the Bayesian restoration of images. IEEE Trans Pattern Anal Mach Intell 6(6):721–741
Besag J (1974) Spatial interaction and the statistical analysis of lattice systems (with discussions). J R Stat Soc Ser B 36:192–236
Winkler G (2003) Image analysis, random fields and Markov chain Monte Carlo methods. Springer, Berlin–Heidelberg
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
Copyright information
© 2011 Springer-Verlag London Limited
About this chapter
Cite this chapter
Chowdhury, A.S., Bhandarkar, S.M. (2011). A Statistical Primer. In: Computer Vision-Guided Virtual Craniofacial Surgery. Advances in Computer Vision and Pattern Recognition. Springer, London. https://doi.org/10.1007/978-0-85729-296-4_3
Download citation
DOI: https://doi.org/10.1007/978-0-85729-296-4_3
Publisher Name: Springer, London
Print ISBN: 978-0-85729-295-7
Online ISBN: 978-0-85729-296-4
eBook Packages: Computer ScienceComputer Science (R0)