Simply stated, this book bridges the gap between statistics and
philosophy. It does this by delineating the conceptual cores of
various statistical methodologies (Bayesian/frequentist statistics,
model selection, machine learning, causal inference, etc.) and
drawing out their philosophical implications. Portraying
statistical inference as an epistemic endeavor to justify
hypotheses about a probabilistic model of a given empirical
problem, the book explains the role of ontological, semantic, and
epistemological assumptions that make such inductive inference
possible. From this perspective, various statistical methodologies
are characterized by their epistemological nature: Bayesian
statistics by internalist epistemology, classical statistics by
externalist epistemology, model selection by pragmatist
epistemology, and deep learning by virtue epistemology. Another
highlight of the book is its analysis of the ontological
assumptions that underpin statistical reasoning, such as the
uniformity of nature, natural kinds, real patterns, possible
worlds, causal structures, etc. Moreover, recent developments in
deep learning indicate that machines are carving out their own
"ontology" (representations) from data, and better understanding
this-a key objective of the book-is crucial for improving these
machines' performance and intelligibility. Key Features Without
assuming any prior knowledge of statistics, discusses philosophical
aspects of traditional as well as cutting-edge statistical
methodologies. Draws parallels between various methods of
statistics and philosophical epistemology, revealing previously
ignored connections between the two disciplines. Written for
students, researchers, and professionals in a wide range of fields,
including philosophy, biology, medicine, statistics and other
social sciences, and business. Originally published in Japanese
with widespread success, has been translated into English by the
author.
General
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