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Maxwellian Probability Distribution Function

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The Distribution Function and its Cumulative

The Maxwellian probability distribution function (PDF) is proportional to where is the molecular speed and is a parameter that depends on the temperature and molecular mass. The normalization factor is given by

So a straightforward definition of the Maxwellian PDF is

We have defined MaxwellPDF so that, if it is called with just one argument, a default value of 1 is used for . It is sufficient to consider just since

For molecular dynamics has the value where is the mass of the molecular species, is the Boltzmann constant, and is the temperature (in Kelvin). This corresponds to,

To calculate random numbers with a probability distribution that conforms to the Maxwellian PDF, we need to compute the cumulative distribution function and invert it.

Here is the cumulative distribution function.

So we can define the cumulative Maxwellian distribution function via

Similar to the case with MaxwellPDF, we have

So we will just work with to generate random numbers. The result can always be rescaled by .

Random Numbers

To generate random numbers according to a given probability distribution function, one can use the inverse of the cumulative distribution function acting on random numbers generated from a uniform distribution. In the case of MaxwellCDF there is no closed form inverse.

However, we can build a numerical function that provides us with an inverse. There are many ways to do this. We describe several possibilities in the pages that follow.

Inverse Series Method with Padé Approximants

[Parts of this example are based on an example given in The Mathematica Journal.]

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