Open AccessJournal Article
Bayesian statistics without tears: A sampling-resampling perspective
TLDR
In this article, a sampling-resampling perspective on Bayesian inference is presented, which has both pedagogic appeal and suggests easily implemented calculation strategies, such as sampling-based methods.Abstract:
Even to the initiated, statistical calculations based on Bayes's Theorem can be daunting because of the numerical integrations required in all but the simplest applications. Moreover, from a teaching perspective, introductions to Bayesian statistics—if they are given at all—are circumscribed by these apparent calculational difficulties. Here we offer a straightforward sampling-resampling perspective on Bayesian inference, which has both pedagogic appeal and suggests easily implemented calculation strategies.read more
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References
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Journal ArticleDOI
Generalized Linear Models
TL;DR: In this paper, the authors used iterative weighted linear regression to obtain maximum likelihood estimates of the parameters with observations distributed according to some exponential family and systematic effects that can be made linear by a suitable transformation.
Journal ArticleDOI
Generalized linear models. 2nd ed.
Peter McCullagh,John A. Nelder +1 more
TL;DR: A class of statistical models that generalizes classical linear models-extending them to include many other models useful in statistical analysis, of particular interest for statisticians in medicine, biology, agriculture, social science, and engineering.
Journal ArticleDOI
Generalized Linear Models
TL;DR: Generalized linear models, 2nd edn By P McCullagh and J A Nelder as mentioned in this paper, 2nd edition, New York: Manning and Hall, 1989 xx + 512 pp £30
Book
Stochastic Simulation
TL;DR: Brian D. Ripley's Stochastic Simulation is a short, yet ambitious, survey of modern simulation techniques, and three themes run throughout the book.