Page 1 of 3 So you know what a bit is – or do you? How much information does a bit carry? What is this "information" stuff anyway? The answers are, unsurprisingly, all contained in the subject called Information Theory, which was invented by one man.
A Programmers Guide To Theory First Draft
There is a more recent version of this:
Now available as a paperback and ebook from Amazon.
Contents
 What Is Computable?
 Finite State Machines
 What is a Turing Machine?
 Computational Complexity
 Noncomputable numbers
 The Transfinite
 Axiom Of Choice
 Lambda Calculus
 Grammar and Torture
 Reverse Polish Notation  RPN
 Introduction to Boolean Logic
 Confronting The Unprovable  Gödel And All That
 The Programmer's Guide to Fractals
 The Programmer's Guide to Chaos*
 Prime Numbers And Primality Testing
 Cellular Automata  The How and Why
 Information Theory
 Coding Theory
 Kolmogorov Complexity
*To be revised
How much data can you store on a 1GByte drive?
The answer is obvious  1GByte of course!
But what does this exactly mean?
How much music, photos, video or books does this equate to?
What does a Gigabyte mean in terms of the real world?
To answer these questions in any meaningful way we need a theory of information and more importantly we need to define a way to measure information.
Until quite recently it wasn't even clear that something called information existed. For a long time we just muddled along and put things down in books, listened to music and didn't really think about how it all worked. It was only when we started to send messages between distant points did it really become apparent that information was a quantity that we might measure.
Today we know that the amount of information in something is measured in bits and this gives you the amount of storage you need to store it and the amount of communications bandwidth we need to transmit it.
This much is easy to say but it raises a whole set of interesting follow on questions.
You can take any set of symbols – the word “the” or the letter “A” for example and specify the amount of information it contains in bits.
How does this work?
How can you say how many bits of information the letter “A” contains?
This is all a little mysterious, although we use the same ideas every day. For example, if you have ever compressed a file then you are making use of information theory. The most amazing thing about information theory is that it is a relatively recent discovery (1948), and it is almost entirely the work of one man, Claude Shannon, who moreorless simply wrote it all down one day.
Claude Shannon (April 30, 1916  February 24, 2001)
Surprise, surprise
Suppose I tell you that the sun rose this morning.
Surprised?
I hope not.
There is a sense in which a statement such as this conveys very little information. You almost don’t need me to tell you it, you already know it. In this sense, if you were already 100% certain that the sun rose this morning and you could even say that me telling you so, carried no information at all!
On the other hand, if I told you that the sun wasn’t going to rise tomorrow then you would presumably be shocked at this unlikely news – never mind about your horror or any other emotion that’s for the psychologists to sort out. We are only interested in the information content of the statement not its implications for the end of the world.
From this point of view it seems reasonable to relate the information in a message to the probability of receiving it. For example, if I am to tell you whether or not the sun rose each morning then on the mornings that I tell you it did you get little information compared to any morning I tell you it didn't. The relative likelihood of a message is related to the amount of information it contains.
This was the key idea that got Shannon started on information theory.
He reasoned like this:
If the probability of message A is p then we can assume that the information contained in the message is some function of p – call it I(p) say.
Now what properties does I(p) have to have?
Suppose I send you the message – now you have I(p)’s worth of information. If I now send you another message B with probability q you now have I(p)+I(q) of information.
But suppose you send me message A and B at the same time. The probability of getting both messages at the same time is p times q.
For example, the probability of getting a head is 0.5 the probability of getting two heads is 0.5 times 0.5 i.e. 0.25.
As it really doesn’t matter if I get message A and then message B or if they arrive together – the information they provide should be the same.
So we are forced to the conclusion that
I(pq)=I(p)+I(q)
If you know some simple maths you will recognise the basic property of logarithms. That is
log(xy)=log(x)+log(y)
What all this means is that if you accept that the information carried by a symbol or a message is a function of its probability then that function has to be a log or something like a log. The reason is that when things happen together their probabilities multiply but the information they carry has to add.
What Shannon did next was to decide that the information in a message A which occurs with probability p should be
I(A)=log_{2}(p)
where the logarithm is taken to the base 2 instead of the more usual base 10 or base e.
You can have two reactions to this sort of argument –
“obvious!”
or
“what?”.
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