Entropy rate and coding length

Consider a binary Markov source with fair initial bit and flip probability $q=1/8$. Its exact $n$-symbol information entropy is $H_n=1+(n-1)h_2(1/8)$. At $n=16$,This includes the uncertainty of the initial bit; the entropy rate itself is $h_2(1/8)\simeq 0.54356444$ bits per symbol. The finite-block difference is $(1-h_2(1/8))/n$.

For any finite block distribution, the entropy lower bound for prefix codes gives expected codeword length $L_n\ge H_n$, while Shannon coding achieves $L_n<H_n+1$. These bounds apply to each block as one source symbol, without requiring consecutive blocks to be independent. Here Huffman coding on all $2^{16}$ possible 16-symbol words gives the independently calculated value $L_{16}\simeq 9.19511361$ bits per block, or $0.57469460$ bits per symbol. The noninteger value is an expected value; each actual codeword has an integer length. As the block length grows, the prefix code bounds squeeze $L_n/n$ towards the entropy rate when an optimal code is used.

An entropy rate below one does not allow every word to fit into a shorter fixed binary word with zero error. For $0<q<1$, all $2^n$ binary words are possible, so an injective fixed-length code needs at least $n$ bits. Compression instead exploits variable codeword lengths, or the vanishing-error convention in reliable source encoding at a rate. These are different guarantees.