Uncertainty: The Soul Of Modeling, Probability & Statistics
by William Briggs /
2016 / English / PDF
3.2 MB Download
This book presents a philosophical approach to probability and
probabilistic thinking, considering the underpinnings of
probabilistic reasoning and modeling, which effectively
underlie everything in data science. The ultimate goal is to
call into question many standard tenets and lay the
philosophical and probabilistic groundwork and infrastructure
for statistical modeling. It is the first book devoted to the
philosophy of data aimed at working scientists and calls for a
new consideration in the practice of probability and statistics
to eliminate what has been referred to as the "Cult of
Statistical Significance."
This book presents a philosophical approach to probability and
probabilistic thinking, considering the underpinnings of
probabilistic reasoning and modeling, which effectively
underlie everything in data science. The ultimate goal is to
call into question many standard tenets and lay the
philosophical and probabilistic groundwork and infrastructure
for statistical modeling. It is the first book devoted to the
philosophy of data aimed at working scientists and calls for a
new consideration in the practice of probability and statistics
to eliminate what has been referred to as the "Cult of
Statistical Significance."The book explains the philosophy of these ideas and not
the mathematics, though there are a handful of mathematical
examples. The topics are logically laid out, starting with
basic philosophy as related to probability, statistics, and
science, and stepping through the key probabilistic ideas
and concepts, and ending with statistical models.
The book explains the philosophy of these ideas and not
the mathematics, though there are a handful of mathematical
examples. The topics are logically laid out, starting with
basic philosophy as related to probability, statistics, and
science, and stepping through the key probabilistic ideas
and concepts, and ending with statistical models.
Its jargon-free approach asserts that standard methods, such as
out-of-the-box regression, cannot help in discovering cause.
This new way of looking at uncertainty ties together disparate
fields ― probability, physics, biology, the “soft” sciences,
computer science ― because each aims at discovering cause (of
effects). It broadens the understanding beyond frequentist and
Bayesian methods to propose a Third Way of modeling.
Its jargon-free approach asserts that standard methods, such as
out-of-the-box regression, cannot help in discovering cause.
This new way of looking at uncertainty ties together disparate
fields ― probability, physics, biology, the “soft” sciences,
computer science ― because each aims at discovering cause (of
effects). It broadens the understanding beyond frequentist and
Bayesian methods to propose a Third Way of modeling.
