Monte
Carlo methods. The basic concept is simple, first you build a
mathematical model of a process which relates the output to one or more
input values. next you run the model with appropriate random values for the
inputs and record the outputs. Finally, you analyse the output values.
For example, if you are looking for mineral deposits, the inputs might be,
the number of boreholes you drill, the probability of success and a
distribution of what you expect to find. The output is a probability
distribution of the resources found. Monte Carlo methods can be a
useful tool for dealing with complex or difficult problems, but remember the
old adage "Garbage-in, garbage-out".
Distributions.
This data was compiled over several software project, in most cases, the
pages follow a similar pattern containing, the profile of the probability
density function, the formula for the descriptive parameters (e.g. mean,
standard deviation etc.), curve fitting, random number generation and an
example.
General
Maths & Stats. Much of this material is intended to support the
sections on Monte Carlo methods and distributions. It includes the
calculation of the gamma function, numerical integration and
net-present-value which is a technique used for, amongst other things, as a
means of comparing projects with different cash flows. The description
of the mean includes a link to a YouTube video.
