random - Bimodal distribution characterization algorithm? -

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Hopefully the algorithm can be used to clearly mark the biomodel distribution, in which 2 different Are the distributions mixed well in different peaks an array of samples? There is something that estimates the meaning of 2, 2 standard deviation, and some kind of robustness, the desired result will be

I am interested in an algorithm that is in any programming language (for embedded controller) Can not apply, existing C or Python library or state package.

Would it be easy if I knew that the two modal is different from the ratio of approximately 3: 1 + - 50%, then the standard deviation is "small" relative to the peak separation, but The pair of peaks can be anywhere in the 100: 1 range?

There are two different possibilities here one is that you have only one distribution that is valid The second is that you are viewing data from two different distributions. The general way to guess later is to say something that is surprisingly, A

Use your approach maximum probability approach to assess or use the Markov chain Monte Carlo methods if you have problems Want to take Bayesian's footsteps. If you tell your beliefs in a bit more detail then I am ready to help you to understand and understand what purpose you want to work and maximize.

Computational models of this type can be intense, so I'm not sure that you want to try a full statistical approach in an embedded controller. A hack may be better fit if the peaks have actually been isolated, so I think trying and identifying two peaks and separating your data between them will be easier and freely average for each delivery and Standard deviation must be assessed.

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