Add to ORKGScientific learning is seen as an iterative process employing Criticism and Estimation. Sampling theory use of predictive distributions for model criticism is examined and also the implications for significance tests and the theory of precise measurement. Normal theory examples and ridge estimates are considered. Predictive checking functions for transformation, serial correlation, and bad values are reviewed as is their relation with Bayesian options. Robustness is seen from a Bayesian view point and examples are given. The bad value problem is also considered and comparison with M estimators is mad
I agree with Rob Kass’ point that we can and should make use of statistical methods developed under ...
This chapter provides a overview of Bayesian inference, mostly emphasising that it is a universal me...
There has recently been much debate about the merits of null hypothesis significance testing (NHST)....
Judgement sampling in market research and opinion polling is standardly criticized as unsatisfactory...
This paper exposits and develops Bayesian methods of model criticism and robustness analysis. The ob...
Bayesian statistics is an approach to data analysis based on Bayes’ theorem, where available knowled...
A learning procedure takes as input a dataset and performs inference for the parameters θ of a model...
Hypothesis testing and model choice are quintessential questions for statistical inference and while...
Often scientific information on various data generating processes are presented in the from of numer...
A learning procedure takes as input a dataset and performs inference for the parameters $\theta$ of ...
A substantial school in the philosophy of science identifies Bayesian inference with inductive infer...
In this paper we review the concepts of Bayesian evidence and Bayes factors, also known as log odds ...
This chapter focuses on Bayesian methods and illustrates both the intrinsic unity of Bayesian thinki...
Even to the initiated, statistical calculations based on Bayes's Theorem can be daunting becaus...
This paper explores the why and what of statistical learning from a computational modelling perspect...
I agree with Rob Kass’ point that we can and should make use of statistical methods developed under ...
This chapter provides a overview of Bayesian inference, mostly emphasising that it is a universal me...
There has recently been much debate about the merits of null hypothesis significance testing (NHST)....
Judgement sampling in market research and opinion polling is standardly criticized as unsatisfactory...
This paper exposits and develops Bayesian methods of model criticism and robustness analysis. The ob...
Bayesian statistics is an approach to data analysis based on Bayes’ theorem, where available knowled...
A learning procedure takes as input a dataset and performs inference for the parameters θ of a model...
Hypothesis testing and model choice are quintessential questions for statistical inference and while...
Often scientific information on various data generating processes are presented in the from of numer...
A learning procedure takes as input a dataset and performs inference for the parameters $\theta$ of ...
A substantial school in the philosophy of science identifies Bayesian inference with inductive infer...
In this paper we review the concepts of Bayesian evidence and Bayes factors, also known as log odds ...
This chapter focuses on Bayesian methods and illustrates both the intrinsic unity of Bayesian thinki...
Even to the initiated, statistical calculations based on Bayes's Theorem can be daunting becaus...
This paper explores the why and what of statistical learning from a computational modelling perspect...
I agree with Rob Kass’ point that we can and should make use of statistical methods developed under ...
This chapter provides a overview of Bayesian inference, mostly emphasising that it is a universal me...
There has recently been much debate about the merits of null hypothesis significance testing (NHST)....