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This book is an extension of the author's first book and serves as
a guide and manual on how to specify and compute 2-, 3-, and
4-Event Bayesian Belief Networks (BBN). It walks the learner
through the steps of fitting and solving fifty BBN numerically,
using mathematical proof. The author wrote this book primarily for
inexperienced learners as well as professionals, while maintaining
a proof-based academic rigor. The author's first book on this
topic, a primer introducing learners to the basic complexities and
nuances associated with learning Bayes' theorem and inverse
probability for the first time, was meant for non-statisticians
unfamiliar with the theorem-as is this book. This new book expands
upon that approach and is meant to be a prescriptive guide for
building BBN and executive decision-making for students and
professionals; intended so that decision-makers can invest their
time and start using this inductive reasoning principle in their
decision-making processes. It highlights the utility of an
algorithm that served as the basis for the first book, and includes
fifty 2-, 3-, and 4-event BBN of numerous variants.
This book is an extension of the author's first book and serves as
a guide and manual on how to specify and compute 2-, 3-, and
4-Event Bayesian Belief Networks (BBN). It walks the learner
through the steps of fitting and solving fifty BBN numerically,
using mathematical proof. The author wrote this book primarily for
inexperienced learners as well as professionals, while maintaining
a proof-based academic rigor. The author's first book on this
topic, a primer introducing learners to the basic complexities and
nuances associated with learning Bayes' theorem and inverse
probability for the first time, was meant for non-statisticians
unfamiliar with the theorem-as is this book. This new book expands
upon that approach and is meant to be a prescriptive guide for
building BBN and executive decision-making for students and
professionals; intended so that decision-makers can invest their
time and start using this inductive reasoning principle in their
decision-making processes. It highlights the utility of an
algorithm that served as the basis for the first book, and includes
fifty 2-, 3-, and 4-event BBN of numerous variants.
Strategic Economic Decision-Making: Using Bayesian Belief Networks
to Solve Complex Problems is a quick primer on the topic that
introduces readers to the basic complexities and nuances associated
with learning Bayes' theory and inverse probability for the first
time. This brief is meant for non-statisticians who are unfamiliar
with Bayes' theorem, walking them through the theoretical phases of
set and sample set selection, the axioms of probability,
probability theory as it pertains to Bayes' theorem, and posterior
probabilities. All of these concepts are explained as they appear
in the methodology of fitting a Bayes' model, and upon completion
of the text readers will be able to mathematically determine
posterior probabilities of multiple independent nodes across any
system available for study. Very little has been published in the
area of discrete Bayes' theory, and this brief will appeal to
non-statisticians conducting research in the fields of engineering,
computing, life sciences, and social sciences.
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