Filling a gap in current Bayesian theory, Statistical Inference: An Integrated Bayesian/Likelihood Approach presents a unified Bayesian treatment of parameter inference and model comparisons that can be used with simple diffuse prior specifications. This novel approach provides new solutions to difficult model comparison problems and offers direct Bayesian counterparts of frequentist t-tests and other standard statistical methods for hypothesis testing.After an overview of the competing theories of statistical inference, the book introduces the Bayes/likelihood approach used throughout. It pr
Introduction to a Special issue on the interaction between Bayesian and Likelihood approaches to st...
A frequentist simultaneous confidence interval procedure requires the predetermination of the compar...
This chapter focuses on Bayesian methods and illustrates both the intrinsic unity of Bayesian thinki...
This book presents an account of the Bayesian and frequentist approaches to statistical inference. I...
We review two foundations of statistical inference, the theory of likelihood and the Bayesian paradi...
This chapter provides a overview of Bayesian inference, mostly emphasising that it is a universal me...
This is a 20 page chapter for the upcoming Handbook of Statistical Systems Biology (D. Balding, M. S...
This richly illustrated textbook covers modern statistical methods with applications in medicine, ep...
Reviews probability and introduces statistical inference. Point and interval estimation. The maximum...
This chapter provides a general overview of Bayesian statistical methods. Topics include the notion ...
Hypothesis testing is a special form of model selection. Once a pair of competing models is fully de...
Aimed at advanced undergraduate and graduate students in mathematics and related disciplines, this b...
This textbook covers the fundamentals of statistical inference and statistical theory including Baye...
In this thesis we present a review of the Bayesian approach to Statistical Inference. In Chapter One...
For many decades, statisticians have made attempts to prepare the Bayesian omelette without breaking...
Introduction to a Special issue on the interaction between Bayesian and Likelihood approaches to st...
A frequentist simultaneous confidence interval procedure requires the predetermination of the compar...
This chapter focuses on Bayesian methods and illustrates both the intrinsic unity of Bayesian thinki...
This book presents an account of the Bayesian and frequentist approaches to statistical inference. I...
We review two foundations of statistical inference, the theory of likelihood and the Bayesian paradi...
This chapter provides a overview of Bayesian inference, mostly emphasising that it is a universal me...
This is a 20 page chapter for the upcoming Handbook of Statistical Systems Biology (D. Balding, M. S...
This richly illustrated textbook covers modern statistical methods with applications in medicine, ep...
Reviews probability and introduces statistical inference. Point and interval estimation. The maximum...
This chapter provides a general overview of Bayesian statistical methods. Topics include the notion ...
Hypothesis testing is a special form of model selection. Once a pair of competing models is fully de...
Aimed at advanced undergraduate and graduate students in mathematics and related disciplines, this b...
This textbook covers the fundamentals of statistical inference and statistical theory including Baye...
In this thesis we present a review of the Bayesian approach to Statistical Inference. In Chapter One...
For many decades, statisticians have made attempts to prepare the Bayesian omelette without breaking...
Introduction to a Special issue on the interaction between Bayesian and Likelihood approaches to st...
A frequentist simultaneous confidence interval procedure requires the predetermination of the compar...
This chapter focuses on Bayesian methods and illustrates both the intrinsic unity of Bayesian thinki...