Statistical Techniques

Now see various statistical techniques to be used in mathematical modelling were told. These techniques were used  to model aircraft variability model.

The Aim was to give an idea, one can develop a mathematical model according to a given scenario.

There are number of problems which can not be modelled by simple statistical/mathematical techniques or it is very difficult to model by these techniques.
Beauty of a model lies, not in its complexity but in its
John von Neumann in the 1940s.
simplicity. For example if one has to make model of a
weapon system, techniques of pure mathematics are not
John von Neumann (Neumann János) (December
28, 1903 – February 8, 1957) was a Hungarian
so handy and one has to opt for other techniques. of Jewish ancestry
this chapter we will be discussing a different technique to the people who hold important
in quantum, physics, functional analysis, set theory, economics,
which is also quite versatile in solving various problems
computer science, numerical
where events are random.

This technique is called
namics (of explosions), statistics and many other
mathematical fields.
Monte Carlo simulation. Computer simulation is one of
Most notably, von Neumann was a pioneer of the
the most powerful techniques to study various types of
modern digital computer and the application of
problems in system analysis.

The conceptual model is
operator theory to quantum mechanics (see Von
Neumann algebra), a member of the Manhattan
the result of the data gathering efforts and is a
Project Team, and creator of game theory and the
formulation in one’s mind (supplemented by notes and
concept of cellular automata. Along with Edward
Teller and Stamslaw Ulam, von Neumann worked out
diagrams) of how a particular system operates. Building
key steps in the nuclear physics involved in
a simulation model means this conceptual model is
thermonuclear reactions and the hydrogen bomb.
converted to a computer model (simulation model).

Making this translation requires two important transitions in one’s thinking. First, the modeller must be able to think of the system in terms of modelling
paradigm supported by the particular modeling software that is being used, second, the different possible ways to model the system should be evaluated to determine the most efficient yet effective way to represent the system. Many problems which can not be solved by mathematical methods can be solved by Monte Carlo simulation.

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