The World is an Average
The simple average
A very different motivation is to overcome a limitation in the accuracy of a measurement. You might have a piston from a car engine and want to measure the diameter as accurately as possible in order to determine is there has been any wear which would indicate you have to buy new pistons when you rebuild the engine. However, the micrometer you have isn't very good, and when you make a measurement you are never sure you are holding the micrometer perpendicular to the axis of the piston, etc. So, to get a little confidence you measure several times and take the average.
Distributions and weighted averages
I certainly don't know what is going on here. Maybe the piston is not round and so you get different values if it's rotated. Maybe I wasn't holding the micrometer perpendicular to the axis of the piston, and occasionally got a high value.
Let's assume for now that the measurements are valid and we want a better way of summarizing them. We then need a different average.
Now you see that the distribution is close to being symmetrical. The antilog of the average of the log values is about 6, indicated by the vertical line. This seems to me to be much better characterization for the diameter of the piston. The value of 6 is also the mode, or most common, of the measurements. Maybe this is a "good" average.
Whenever a collection of data has a wide variation, with many values close to zero, and when a value can't be negative, you should try a log plot to see if the geometric average is appropriate. Often biologic data have these properties. One example is the concentration of antibodies that are produced as the result of a vaccination. Another example is the distribution of option prices with time (see work of Black, Scholes, and Merton which led to the 1997 Nobel Prize in Economics).
A second red flag is the thought: "if you can distort the x-axis of the distribution using the log function, why not another function?" Why not indeed. A comprehensive analysis of a collection of data with variation starts with the determination of the shape of the distribution. This shape will then suggest functions of the x-variable that will give a symmetrical distribution. Of course this requires a many data points to make precise conclusions, but there are certainly procedures for doing the analysis. Fortunately most of the time date is distributed in the symmetrical normal or Gaussian distribution, for which the arithmetic average is appropriate, as discussed in great detail in all statistics texts.
Averages over time
The implicit assumption here is that there is a short term "random" fluctuation in price superimposed on a long term "real" trend. The obvious mathematical tool to discover the trend is an average. The question is: what kind of average and how do I do it?
You can think of this process as looking at the raw data through a window five days wide, the blue rectangle superimposed on the graph.
However, an immediate question is: "how did you pick five instead of 2, 3, or 7 days". Doesn't it seem artificial to give as much weight to the price five days ago as the price today but no weight to the price 6 days ago?
You may not want to continue the window out to the distant past, it's just too much work, and the values more than 8 days in the past (in this example) don't contribute much to the average anyway. However wide the window, you do have to adjust the weighting factors so that their sum is one.
This decreasing average may seem more natural, since the influence of the old prices on the moving average gradually decreases. However, you still have to decide how fast the influence decreases, an arbitrary decision unless you know something about stock behavior
Stock prices don't follow the laws of physics, or any other laws I know of. Some people use the terms of physics, e.g. momentum, to describe the behavior of stock prices. At best these are analogies, at worse nonsense. The mathematical and graphic tools I have described enable you to explore models of stock behavior (or whatever), but don't pretend to actually predict anything using basic laws.
A little notation
Averaging in space
Usually a lens system would be directed at a much more interesting and complicated object. The system could be on a rocket and it might be "looking" at the planet Mars. However, Mars, or any object, can be thought of as a series of "points of light", which together make up the image. If a single point of light is blurred, each of the points of light that make an image also are blurred, and thus the image, the sum of all the points, is also blurred. Any system that has the property that the final result is just the simple sum of all the components (in this case the sum of all the point spread functions of parts of the object). A non-linear system is very difficult to work with.
The lens has created a moving average, or a convolution over space, even if in this case we would rather have the raw data and not the average. But all sensory devices create an average, including our own eyes and ears. Thus, to us, and all other animals, the world is seen and heard as an average of the real world.
A natural question might be, can this process be undone? Since the convolution is a set of simultaneous linear equations, the original values, can in general be computed by solving this set.
On the left we see an image of a dog through an imperfect lens: the real image has been convoluted by the lens.
In order to obtain the deconvolution you need the "window" values. The window is the point spread function, which can be obtained from the image of a point. In this case the point spread function is known to be a Gaussian (because I created it using the Gaussian function).
As you might guess, deconvolution could be very important for many scientific and technical applications, and thus both the theoretical and practical aspects are well studied. A very powerful computational method uses the Fast Fourier Transform (FFT) of the image and the point spread function. The FFT of the image is a series of numbers that represent the relative amounts of sin waves of decreasing wavelengths that, when added, give the best representation of an image. It turns out that dividing the FFT of the blurred image by the FFT of the point spread function gives the FFT of the original image. Finally, the image can be calculated from its FFT.
Deconvolution might seem to be so easy that no one would pay for a good lens, or work to get the subject in focus before snapping the picture. Unfortunately there are two problems. A minor problem is that it takes a great many calculations to deconvolute an image. Computers are so powerful and cheap that this would only mean that it would typically take tens of seconds to minutes to deconvolute each image, but it would still be a bother.
A more serious problem is that even small levels of noise, i.e. random variations in the pixel intensities, causes big errors in the deconvoluted image. Deconvolution thus improves an image, but in practice not back to its best representation.
Thus it is important to get the best image possible, and then do a deconvolution if the image is very important. As an example, images taken by cameras sent to Mars are very important, and a lot of work is done on them to obtain the most detail possible.
All images of the real world are obtained by cameras or eyes, and all these devices introduce distortion defined by a point spread function.
Q.E.D., the world as we know it is an average.
<back to home page>