Another title in the reissued Oxford Classic Texts in the Physical Sciences series, Jeffrey's Theory of Probability, first published in 1939, was the first to develop a fundamental theory of scientific inference based on the ideas of Bayesian statistics. His ideas were way ahead of their time and it is only in the past ten years that the subject of Bayes' factors has been significantly developed and extended. Until recently the two schools of statistics were distinctly different and set apart. (...) Recent work has changed all that and today's graduate students and researchers all require an understanding of Bayesian ideas. This book is their starting point. (shrink)

If cosmology is to obtain knowledge about the whole universe, it faces an underdetermination problem: Alternative space-time models are compatible with our evidence. The problem can be avoided though, if there are good reasons to adopt the Cosmological Principle (CP), because, assuming the principle, one can confine oneself to the small class of homogeneous and isotropic space-time models. The aim of this paper is to ask whether there are good reasons to adopt the Cosmological Principle in order to avoid underdetermination (...) in cosmology. Various strategies to justify the CP are examined. For instance, arguments to the effect that the truth of the CP follows generically from a large set of initial conditions; an inference to the best explanation; and an inductive strategy are assessed. I conclude that a convincing justification of the CP has not yet been established, but this claim is contingent on a number of results that may have to be revised in the future. (shrink)

In several papers, John Norton has argued that Bayesianism cannot handle ignorance adequately due to its inability to distinguish between neutral and disconfirming evidence. He argued that this inability sows confusion in, e.g., anthropic reasoning in cosmology or the Doomsday argument, by allowing one to draw unwarranted conclusions from a lack of knowledge. Norton has suggested criteria for a candidate for representation of neutral support. Imprecise credences (families of credal probability functions) constitute a Bayesian-friendly framework that allows us to avoid (...) inadequate neutral priors and better handle ignorance. The imprecise model generally agrees with Norton's representation of ignorance but requires that his criterion of self-duality be reformulated or abandoned. (shrink)

The article revisits the large n problem as it relates to the Jeffreys-Lindley paradox to compare the frequentist, Bayesian, and likelihoodist approaches to inference and evidence. It is argued that what is fallacious is to interpret a rejection of as providing the same evidence for a particular alternative, irrespective of n; this is an example of the fallacy of rejection. Moreover, the Bayesian and likelihoodist approaches are shown to be susceptible to the fallacy of acceptance. The key difference is that (...) in frequentist testing the severity evaluation circumvents both fallacies but no such principled remedy exists for the other approaches. (shrink)

Since the discovery of dark matter in the 1980s, multiple experiments have been set up to detect dark matter particles through some other mode than gravity. Particle physicists provide detailed jus...

With the ever-growing quality of observational data in astronomy, the complexity of astrophysical models has been increasing in turn. This trend raises the question: Are there still reasons to prefer simpler models if the final goal is an actual model-target comparison? I argue for two aspects in which astrophysical research may favor models having reduced complexity: first, to address the problem of determining the values of adjustable parameters and, second, to pave the way for a validation of the model based (...) on the modeler’s understanding of the scope of the model and the critical processes on the target’s side. (shrink)