Once you’re comfortable with 2-arrays and 2-matrices, you can **move up a dimension or two**, to 4-arrays or 4-tensors.

You can move up to a 3-array / 3-tensor just by imagining a matrix which “extends back into the blackboard”. Like a 5 × 5 matrix. With another 5 × 5 matrix behind it. And another 5 × 5 matrix behind that with 25 more entries. Etc.

The other way is to imagine “Tables of tables of tables of tables … of tables of tables of tables.” This imagination technique is infinitely extensible.

If that looks complicated, it’s just because simple recursion can produce convoluted outputs. Reading the LaTeX (alt text) is definitely harder than writing it was. (I just cut & paste \begin{bmatrix} stuff \end{bmatrix} inside other \begin{bmatrix} … \end{bmatrix}.)

(The technical difference between an array and a tensor: an array is a block which holds data. A tensor is a block of numbers which (linearly) transform matrices / vectors / tensors. **Array = noun. Tensor = verb.**)

As the last picture — the most important one — demonstrates, a 4-array can be filled with completely plain, ordinary, pedestrian information like age, weight, height.

Inside each of the yellow or blue boxes in the earlier pictures, is a **datum**. What calls for the high-dimensional array is **the ***structure* and inter-relationships of the infos. Age, height, sex, and weight each *belongs_to* a particular person, in an object-oriented sense. And one can *marginalise*, in a statistical sense, over any of those variables — consider all the ages of the people surveyed, for example.

One last takeaway:

- Normal, pedestrian, run-of-the-mill, everyday descriptions of things = high-dimensional arrays of varying data types.

**Normal people **speak about and **conceive of information which fits high-D arrays all the time.** “Attached” (in the fibre sense) to any person you know is a huge database of facts. Not to mention data-intensive visual information like parameterisations of the surface of their face, which we naturally process in an Augenblick.

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**Tags:** 4-D, array, Data, linear transformations, matrices, matrix, matrix algebra, object orientation, object-oriented programming, R, tensor