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This image show a histogram (200 bins) of accumulated distances from a radar distance meter (very noisy). The peak around 7 meters is an object. At thought this looked kind of like a normal distribution, at least if you ignore values <4m (which for this application is reasonable).

What I am trying to do is to filter out true distances based on the probability distribution.

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    Good grief... that indeed is noisy. In any event, if you don't get that many answers here, you could also try @ [CrossValidated](http://stats.stackexchange.com/).2010-12-13
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    @J.M. Yeah, very noisy indeed, I can give CV a try too..2010-12-13
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    I find the title misleading. Maybe you mean "Which probability distribution might this be?".2010-12-13
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    Thanks for cross-posting on Cross Validated: here's the link to additional answers: http://stats.stackexchange.com/questions/5430/how-would-you-filter-this2011-05-18

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If it wasn't for the spike at 0 and the mode at around seven, it would look roughly lognormal to me

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The density function of distance-from-mean of a bivariate normal distribution takes the form $Ax e^{-Bx^2}$, which look pretty much like your noisy graph.

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    bivariate? Its really only one dimention - distance. I found that a log - normal distribution (http://en.wikipedia.org/wiki/Log-normal_distribution) makes a decent fit..2010-12-13
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    Well, some radar dishes go round and round...2010-12-13
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    Ok, I see your point. Maybe I should have pointed out that it is a 1D radar.2010-12-13
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I would fit a straight line to the bins near but outside the peak and subtract this from the data, claiming that this level is the background. Then you can do a 2D fit of the Gaussian parameters (mean and sigma) and see how it turns out.

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This is a (tri-modal) mixture distribution.