[Book Summary - Information Theory] Information Theory: A Tutorial Introduction, Ch 5 by James V. Stone

1. Entropy: Average surprisal 

2. Discrete variables  

• Continuous variables: divide the continuum into bins  
• Depending the number of bins, entropy changes
• If the number of bins increases, thus each bin size being smaller, entropy increases due to more options being made.

• Differential entropy
• Transforming continuous variables
• Changing the range of a discrete set of variables doesn't change the entropy
• Example: In the case of a binary dice, which features only 0 and 1, the output should be 0 or 1 even though either of them is divided by infinite numbers between 0 and 1 
• Changing the range of a continuous variable does, because the entropy is based on bin-width
• Doubling the range -> doubles the number of bins -> adds one bit, even if half of the bins aren't used
• What about adding a constant? 
• As the term "constant" implies, It doesn't change entropy because range (variance) remains the same. 
• Maximum entropy distributions
• What distribution can we engineer so that entropy is highest? 
• Fixed upper/lower bounds 
• Fixed mean, with all values >= 0 ==> exponential 
• Fixed variance (e.g., power) 
• Back to differential entropy
• Infinitely accurately…
• What in practice limits 'bin sizes'?
• Noise!
• If the amount of noise increase, it is getting harder to know what the actual signal was
• How does noise limit bin-sized connection to transmission of information in language?
• More noise, less precision due to the number of bins being smaller because of noise (i.e., log1/delta x decreases)
• Zipf's law: Infrequent words => longer, lexicon is limited        

• Bin size => Amount of signal!