Probability and Scientific Inference

Sampling Probability and Inference

The second part of the book looks into the probabilistic foundation of statistical analysis, which originates in probabilistic sampling, and introduces the reader to the arena of hypothesis testing. Chapter 5 explores the main random and controllable source of error, sampling, as opposed to non-sampling errors, potentially very dangerous and unknown. It shows the statistical advantages of extra...

Conditional Probability and Defeasible Inference

We offer a probabilistic model of rational consequence relations (Lehmann and Magidor, 1990) by appealing to the extension of the classical Ramsey-Adams test proposed by Vann McGee in (McGee, 1994). Previous and influential models of non-monotonic consequence relations have been produced in terms of the dynamics of expectations (Gärdenfors and Makinson, 1994), (Gärdenfors, 1993). ‘Expectation’ ...

Supraclassical Inference without Probability

Approximate Inference and Scientific Method

A new identification criterion, motivated by notions of successively improving approximations in the philosophy of science, is defined. It is shown that the class of recursive functions is identifiable under this criterion. This result is extended to permit somewhat more realistic types of data than usual. This criterion is then modified to consider restrictions on the quality of approximations...

Visualizing Scientific Inference

The sciences use a wide range of visual devices, practices, and imaging technologies. This diversity points to an important repertoire of visual methods that scientists use to adapt representations to meet the varied demands that their work places on cognitive processes. This paper identifies key features of the use of visualization in a range of scientific domains and considers the implication...