Belief, Evidence, And Uncertainty: Problems Of Epistemic Inference (springerbriefs In Philosophy)
by Prasanta S. Bandyopadhyay /
2016 / English / PDF
2.3 MB Download
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 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.
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.
The book concludes with a technical appendix which introduces an
evidential approach to multi-model inference as an alternative to
Bayesian model averaging.