Add to ORKGThis chapter presents the basic concepts and methods you need in order to estimate parameters, establish confidence limits, and choose among competing hypotheses and models. It defines likelihood and discusses frequentist, Bayesian, and information-theoretic inference based on likelihood.
ABSTRACT: The Likelihood Theory of Evidence (LTE) says, roughly, that only likelihoods matter to the...
An introduction to model fitting, starting with a review of the linear model and proceeding to a def...
The notion of evidence is of great importance, but there are substantial disagreements about how it ...
We introduce the statistical concept known as likelihood and discuss how it underlies common Frequen...
We review two foundations of statistical inference, the theory of likelihood and the Bayesian paradi...
This textbook covers the fundamentals of statistical inference and statistical theory including Baye...
This richly illustrated textbook covers modern statistical methods with applications in medicine, ep...
Filling a gap in current Bayesian theory, Statistical Inference: An Integrated Bayesian/Likelihood A...
This chapter provides a overview of Bayesian inference, mostly emphasising that it is a universal me...
I review the classical theory of likelihood based inference and consider how it is being extended an...
This chapter provides an overview of the Bayesian approach to data analysis, modeling, and statistic...
This book takes a fresh look at the popular and well-established method of maximum likelihood for st...
This chapter provides a general overview of Bayesian statistical methods. Topics include the notion ...
Aimed at advanced undergraduate and graduate students in mathematics and related disciplines, this b...
In both classical and Bayesian approaches, statistical inference is unified and generalized by the c...
ABSTRACT: The Likelihood Theory of Evidence (LTE) says, roughly, that only likelihoods matter to the...
An introduction to model fitting, starting with a review of the linear model and proceeding to a def...
The notion of evidence is of great importance, but there are substantial disagreements about how it ...
We introduce the statistical concept known as likelihood and discuss how it underlies common Frequen...
We review two foundations of statistical inference, the theory of likelihood and the Bayesian paradi...
This textbook covers the fundamentals of statistical inference and statistical theory including Baye...
This richly illustrated textbook covers modern statistical methods with applications in medicine, ep...
Filling a gap in current Bayesian theory, Statistical Inference: An Integrated Bayesian/Likelihood A...
This chapter provides a overview of Bayesian inference, mostly emphasising that it is a universal me...
I review the classical theory of likelihood based inference and consider how it is being extended an...
This chapter provides an overview of the Bayesian approach to data analysis, modeling, and statistic...
This book takes a fresh look at the popular and well-established method of maximum likelihood for st...
This chapter provides a general overview of Bayesian statistical methods. Topics include the notion ...
Aimed at advanced undergraduate and graduate students in mathematics and related disciplines, this b...
In both classical and Bayesian approaches, statistical inference is unified and generalized by the c...
ABSTRACT: The Likelihood Theory of Evidence (LTE) says, roughly, that only likelihoods matter to the...
An introduction to model fitting, starting with a review of the linear model and proceeding to a def...
The notion of evidence is of great importance, but there are substantial disagreements about how it ...