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Probabilistic modeling represents a subject spanning many branches
of mathematics, economics, and computer science to connect pure
mathematics with applied sciences. Operational research also relies
on this connection to enable the improvement of business functions
and decision making. Analyzing Risk through Probabilistic Modeling
in Operations Research is an authoritative reference publication
discussing the various challenges in management and decision
science. Featuring exhaustive coverage on a range of topics within
operational research including, but not limited to, decision
analysis, data mining, process modeling, probabilistic
interpolation and extrapolation, and optimization methods, this
book is an essential reference source for decision makers,
academicians, researchers, advanced-level students, technology
developers, and government officials interested in the
implementation of probabilistic modeling in various business
applications.
Shape representation and reconstruction is a vital aspect of modern
computer science applications. New modeling methods and techniques
can significantly optimize these processes. Probabilistic Nodes
Combination (PNC) for Object Modeling and Contour Reconstruction is
an innovative reference source that examines the latest trends in
2D curve interpolation and modeling methodologies. Focusing on a
range of pertinent topics such as 3D surface modeling,
high-dimensional data, and numerical methods, this is an ideal
publication for programmers, researchers, students, and
practitioners interested in emerging methods in object modeling and
contour reconstruction.
Probabilistic modeling represents a subject arising in many
branches of mathematics, economics, and computer science. Such
modeling connects pure mathematics with applied sciences.
Similarly, data analyzing and statistics are situated on the border
between pure mathematics and applied sciences. Therefore, when
probabilistic modeling meets statistics, it is a very interesting
occasion that has gained much research recently. With the increase
of these technologies in life and work, it has become somewhat
essential in the workplace to have planning, timetabling,
scheduling, decision making, optimization, simulation, data
analysis, and risk analysis and process modeling. However, there
are still many difficulties and challenges that arrive in these
sectors during the process of planning or decision making. There
continues to be the need for more research on the impact of such
probabilistic modeling with other approaches. Analyzing Data
Through Probabilistic Modeling in Statistics is an essential
reference source that builds on the available literature in the
field of probabilistic modeling, statistics, operational research,
planning and scheduling, data extrapolation in decision making,
probabilistic interpolation and extrapolation in simulation,
stochastic processes, and decision analysis. This text will provide
the resources necessary for economics and management sciences and
for mathematics and computer sciences. This book is ideal for
interested technology developers, decision makers, mathematicians,
statisticians and practitioners, stakeholders, researchers,
academicians, and students looking to further their research
exposure to pertinent topics in operations research and
probabilistic modeling.
Probabilistic modeling represents a subject arising in many
branches of mathematics, economics, and computer science. Such
modeling connects pure mathematics with applied sciences.
Similarly, data analyzing and statistics are situated on the border
between pure mathematics and applied sciences. Therefore, when
probabilistic modeling meets statistics, it is a very interesting
occasion that has gained much research recently. With the increase
of these technologies in life and work, it has become somewhat
essential in the workplace to have planning, timetabling,
scheduling, decision making, optimization, simulation, data
analysis, and risk analysis and process modeling. However, there
are still many difficulties and challenges that arrive in these
sectors during the process of planning or decision making. There
continues to be the need for more research on the impact of such
probabilistic modeling with other approaches. Analyzing Data
Through Probabilistic Modeling in Statistics is an essential
reference source that builds on the available literature in the
field of probabilistic modeling, statistics, operational research,
planning and scheduling, data extrapolation in decision making,
probabilistic interpolation and extrapolation in simulation,
stochastic processes, and decision analysis. This text will provide
the resources necessary for economics and management sciences and
for mathematics and computer sciences. This book is ideal for
interested technology developers, decision makers, mathematicians,
statisticians and practitioners, stakeholders, researchers,
academicians, and students looking to further their research
exposure to pertinent topics in operations research and
probabilistic modeling.
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