2 Chapter 1 Probability Models in Electrical and Computer Engineering
This chapter introduces probability models and shows how they differ from the
deterministic models that are pervasive in engineering. The key properties of the no-
tion of probability are developed, and various examples from electrical and computer
engineering, where probability models play a key role, are presented. Section 1.6 gives
an overview of the book.
1.1 MATHEMATICAL MODELS AS TOOLS IN ANALYSIS AND DESIGN
The design or modification of any complex system involves the making of choices from
various feasible alternatives. Choices are made on the basis of criteria such as cost, re-
liability, and performance.The quantitative evaluation of these criteria is seldom made
through the actual implementation and experimental evaluation of the alternative con-
figurations. Instead, decisions are made based on estimates that are obtained using
models of the alternatives.
A model is an approximate representation of a physical situation. A model at-
tempts to explain observed behavior using a set of simple and understandable rules.
These rules can be used to predict the outcome of experiments involving the given
physical situation. A useful model explains all relevant aspects of a given situation.
Such models can be used instead of experiments to answer questions regarding the
given situation. Models therefore allow the engineer to avoid the costs of experimenta-
tion, namely, labor, equipment, and time.
Mathematical models are used when the observational phenomenon has measur-
able properties. A mathematical model consists of a set of assumptions about how a
system or physical process works. These assumptions are stated in the form of mathe-
matical relations involving the important parameters and variables of the system. The
conditions under which an experiment involving the system is carried out determine the
“givens” in the mathematical relations, and the solution of these relations allows us to
predict the measurements that would be obtained if the experiment were performed.
Mathematical models are used extensively by engineers in guiding system design
and modification decisions. Intuition and rules of thumb are not always reliable in pre-
dicting the performance of complex and novel systems, and experimentation is not pos-
sible during the initial phases of a system design. Furthermore, the cost of extensive
experimentation in existing systems frequently proves to be prohibitive. The availabil-
ity of adequate models for the components of a complex system combined with a
knowledge of their interactions allows the scientist and engineer to develop an overall
mathematical model for the system. It is then possible to quickly and inexpensively an-
swer questions about the performance of complex systems. Indeed, computer pro-
grams for obtaining the solution of mathematical models form the basis of many
computer-aided analysis and design systems.
In order to be useful, a model must fit the facts of a given situation.Therefore the
process of developing and validating a model necessarily consists of a series of experi-
ments and model modifications as shown in Fig. 1.1. Each experiment investigates a
certain aspect of the phenomenon under investigation and involves the taking of ob-
servations and measurements under a specified set of conditions. The model is used
to predict the outcome of the experiment, and these predictions are compared with
the actual observations that result when the experiment is carried out. If there is a