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Belief, Evidence, and Uncertainty - Problems of Epistemic Inference (Paperback, 1st ed. 2016)
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Belief, Evidence, and Uncertainty - Problems of Epistemic Inference (Paperback, 1st ed. 2016)
Series: SpringerBriefs in Philosophy
Expected to ship within 10 - 15 working days
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This work breaks new ground by carefully distinguishing the
concepts of belief, confirmation, and evidence and then integrating
them into a better understanding of personal and scientific
epistemologies. It outlines a probabilistic framework in which
subjective features of personal knowledge and objective features of
public knowledge have their true place. It also discusses the
bearings of some statistical theorems on both formal and
traditional epistemologies while showing how some of the existing
paradoxes in both can be resolved with the help of this
framework.This book has two central aims: First, to make precise a
distinction between the concepts of confirmation and evidence and
to argue that failure to recognize this distinction is the source
of certain otherwise intractable epistemological problems. The
second goal is to demonstrate to philosophers the fundamental
importance of statistical and probabilistic methods, at stake in
the uncertain conditions in which for the most part we lead our
lives, not simply to inferential practice in science, where they
are now standard, but to epistemic inference in other contexts as
well. Although the argument is rigorous, it is also accessible. No
technical knowledge beyond the rudiments of probability theory,
arithmetic, and algebra is presupposed, otherwise unfamiliar terms
are always defined and a number of concrete examples are given. At
the same time, fresh analyses are offered with a discussion of
statistical and epistemic reasoning by philosophers. This book will
also be of interest to scientists and statisticians looking for a
larger view of their own inferential techniques.The book concludes
with a technical appendix which introduces an evidential approach
to multi-model inference as an alternative to Bayesian model
averaging.
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