Add to ORKGIn this paper the likelihood function is considered to be the primary source of the objectivity of a Bayesian method. The necessity of using the expected behaviour of the likelihood function for the choice of the prior distribution is emphasized. Numerical examples, including seasonal adjustment of time series, are given to illustrate the practical utility of the common-sense approach to Bayesian statistics proposed in this pape
Contemporary Bayesian forecasting methods draw on foundations in subjective probability and preferen...
This is the first of two articles which apply certain principles of inference to a practical, financ...
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...
In both classical and Bayesian approaches, statistical inference is unified and generalized by the c...
This richly illustrated textbook covers modern statistical methods with applications in medicine, ep...
In this thesis we present a review of the Bayesian approach to Statistical Inference. In Chapter One...
This introduction to Bayesian statistics presents the main concepts as well as the principal reasons...
The aim of this thesis is to cover the basics of Bayesian inference. Bayesian logic is to consider p...
Despite a shared commitment to using Bayes ’ theorem as the basis for inductive inference, Bayesian ...
This paper considers how the concepts of likelihood and identification became part of Bayesian theor...
This paper explains why it is important to understand Bayesian techniques and how they are advantage...
The Bayesian approach allows an intuitive way to derive the methods of statistics. Probability is de...
Bayesian statistics is an approach to data analysis based on Bayes’ theorem, where available knowled...
This chapter provides a general overview of Bayesian statistical methods. Topics include the notion ...
Contemporary Bayesian forecasting methods draw on foundations in subjective probability and preferen...
This is the first of two articles which apply certain principles of inference to a practical, financ...
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...
In both classical and Bayesian approaches, statistical inference is unified and generalized by the c...
This richly illustrated textbook covers modern statistical methods with applications in medicine, ep...
In this thesis we present a review of the Bayesian approach to Statistical Inference. In Chapter One...
This introduction to Bayesian statistics presents the main concepts as well as the principal reasons...
The aim of this thesis is to cover the basics of Bayesian inference. Bayesian logic is to consider p...
Despite a shared commitment to using Bayes ’ theorem as the basis for inductive inference, Bayesian ...
This paper considers how the concepts of likelihood and identification became part of Bayesian theor...
This paper explains why it is important to understand Bayesian techniques and how they are advantage...
The Bayesian approach allows an intuitive way to derive the methods of statistics. Probability is de...
Bayesian statistics is an approach to data analysis based on Bayes’ theorem, where available knowled...
This chapter provides a general overview of Bayesian statistical methods. Topics include the notion ...
Contemporary Bayesian forecasting methods draw on foundations in subjective probability and preferen...
This is the first of two articles which apply certain principles of inference to a practical, financ...
We introduce the statistical concept known as likelihood and discuss how it underlies common Frequen...