Add to ORKGWe introduce the statistical concept known as likelihood and discuss how it underlies common Frequentist and Bayesian statistical methods. This article is suitable for researchers interested in understanding the basis of their statistical tools, and is also ideal for teachers to use in their classrooms to introduce the topic to students at a conceptual level
Parameter estimation and model fitting underlie many statistical procedures. Whether the objective i...
There is a strong upsurge in the use of Bayesian methods in applied statistical analysis, yet most i...
We highlight the different uses of the word 'likelihood' that have arisen in statistics and meteorol...
This textbook covers the fundamentals of statistical inference and statistical theory including Baye...
We review two foundations of statistical inference, the theory of likelihood and the Bayesian paradi...
This chapter presents the basic concepts and methods you need in order to estimate parameters, estab...
This richly illustrated textbook covers modern statistical methods with applications in medicine, ep...
The notion of evidence is of great importance, but there are substantial disagreements about how it ...
This paper considers how the concepts of likelihood and identification became part of Bayesian theor...
Introduction to Statistical Thought grew out of my teaching graduate and undergraduate statistics co...
Introduction to a Special issue on the interaction between Bayesian and Likelihood approaches to st...
This tutorial on Bayesian inference targets psychological researchers who are trained in the null hy...
Summary. Statistical inference is the basic toolkit used throughout the whole book. This chapter is ...
In this paper the likelihood function is considered to be the primary source of the objectivity of a...
University courses in elementary statistics are usually taught from a frequentist perspective. In th...
Parameter estimation and model fitting underlie many statistical procedures. Whether the objective i...
There is a strong upsurge in the use of Bayesian methods in applied statistical analysis, yet most i...
We highlight the different uses of the word 'likelihood' that have arisen in statistics and meteorol...
This textbook covers the fundamentals of statistical inference and statistical theory including Baye...
We review two foundations of statistical inference, the theory of likelihood and the Bayesian paradi...
This chapter presents the basic concepts and methods you need in order to estimate parameters, estab...
This richly illustrated textbook covers modern statistical methods with applications in medicine, ep...
The notion of evidence is of great importance, but there are substantial disagreements about how it ...
This paper considers how the concepts of likelihood and identification became part of Bayesian theor...
Introduction to Statistical Thought grew out of my teaching graduate and undergraduate statistics co...
Introduction to a Special issue on the interaction between Bayesian and Likelihood approaches to st...
This tutorial on Bayesian inference targets psychological researchers who are trained in the null hy...
Summary. Statistical inference is the basic toolkit used throughout the whole book. This chapter is ...
In this paper the likelihood function is considered to be the primary source of the objectivity of a...
University courses in elementary statistics are usually taught from a frequentist perspective. In th...
Parameter estimation and model fitting underlie many statistical procedures. Whether the objective i...
There is a strong upsurge in the use of Bayesian methods in applied statistical analysis, yet most i...
We highlight the different uses of the word 'likelihood' that have arisen in statistics and meteorol...