What does it mean exactly, to "build a statistical model" of, say, a series of images? I would like a meaningful and concise explanation for what it means exactly, when someone says, "We built a statistical model of all our images". 
I overheard this, (and keep overhearing that phrase), but I am not sure how/what that entails exactly. 

EDIT: Context:
The problem set here is as follows. From a UAV, video imagery of corn fields is collected en-masse. The problem is to be able to tell when a pest (usually a small-medium sized rodent/mammal) is moving. Usually they just took the difference between consecutive images. However in this case, many false alarms happened because leaves might sway in the wind, small changes occur that are not related to the rodent moving, etc. So to this end, they said "Instead of the difference between frames method, we instead build a robust statistical model of the images we have". So what does that mean exactly?
This is the context. Thank you.
 A: I think they are modelling each pixel as a mixture of gaussians - this is a model of the background
see eg opencv
http://docs.opencv.org/modules/video/doc/motion_analysis_and_object_tracking.html
BackgroundSubtractorMOG¶
An improved adaptive background mixture model for real-time tracking with shadow detection, Proc. 2nd European Workshop on Advanced Video-Based Surveillance Systems, 2001: http://personal.ee.surrey.ac.uk/Personal/R.Bowden/publications/avbs01/avbs01.pdf
My simplified understanding:
you model each pixel (separately) as  a MOG. Take 1  gaussian case- for each pixel you calculate running average, and standard deviation. So now you calculate z-score of intensity value wrt your gaussian model - if its an outlier you declare "rat"
so previously you were marking X[i,j,t+1]-X[i,j,t]>threshold ="rat"
instead (X[i,j,t+1]-mean[i,j])/stddev[i,j] > threshold ="rat"
[and more complicated with real mixture of gaussians]
To clarify- what is being built is a statistical model of a "background" pixel. The current method can be interpreted as assuming each pixel has constant intensity: taking difference (in time) identifies the foreground "objects". Next level up, we model background pixel values as normally distributed: we estimate the mean and standard deviation of each pixel by a running average calculation. This basically allows us to ignore "typical variations" - ie areas which are constantly changing pixel values are ignored (eg leaves on tree?) - you need a bigger change there than in areas of low "typical variation". Finally, Mixture of Gaussians allows you cope with more complicated "typical patterns of variation".
A: The statement is somewhat ambiguous, because it does not specify what kind of model they built.  But what they are saying is that they have found some PDF to describe the series of images.  It might be using one of the techniques you describe, or it could be using something else like for example a restricted boltzman machine, just to pick one.  Because images do not lend themselves very naturally to normal distributions, picking the kind of model used is a big part of building the model.
A bit more background if it isn't obvious: the standard first step is to take the image and represent it as a series of pixel values (0-1, 0-255, doesn't really matter), either as a 2-D matrix of pixel intensities, a 3-D matrix if it's color, or just flatten it to a vector.
What you do from there is the real trick.  There is no standard statistical model for representing images, so if the author of the statement wants to be explicit they need to specify the model they used.  So yes -- that's a very fair question.  But conceptually having some PDF to describe the image set makes sense just and is meaningful regardless of what the underlying model is.
