Ruka hadi maudhui
The Lumenary
Shahada ya kwanza (juu)Applied MathematicsImepangwa

Information theory

Entropy and information, Shannon's source coding theorem, prefix codes and Huffman coding, mutual information, channel capacity and the noisy-channel coding theorem.

math.applied.information-theory

Onyesha kila kinachohitajika kufikia mada hii

Somo la mada hii bado halijaandikwa. Muhtasari ulio hapa chini ni sehemu ya ramani ya mtaala na haujakaguliwa.

Inajengwa juu ya

Probability theory

Masharti ya awali ya lazima 41 (hadi chini kabisa)

Utajifunza

  • Compute entropy, joint and conditional entropy, and mutual information.
  • Construct Huffman codes and prove Kraft's inequality.
  • State the source coding and noisy-channel coding theorems and compute the capacity of the binary symmetric channel.

Fasili

  • The entropy of a discrete random variable X is H(X) = −Σ p(x) log₂ p(x).
  • The mutual information of X and Y is I(X; Y) = H(X) − H(X | Y).
  • The capacity of a channel is the maximum of I(X; Y) over all input distributions.

Nadharia

  • The expected length of an optimal binary prefix code for X lies between H(X) and H(X) + 1 bits.
  • Kraft: a binary prefix code with codeword lengths ℓᵢ exists if and only if Σ 2^(−ℓᵢ) ≤ 1.
  • The binary symmetric channel with crossover probability p has capacity 1 − H(p).
  • Noisy-channel coding theorem: reliable communication is possible at every rate below capacity and impossible at rates above it.

Mawazo potofu ya kawaida

  • Equating information with meaning; Shannon information measures uncertainty.
  • Thinking a compression method can shrink every file.

Ugunduzi wa hiari

Njia za kujifunza

Usomaji zaidi

  • Thomas M. Cover and Joy A. Thomas, Elements of Information Theory (1991)
  • David J. C. MacKay, Information Theory, Inference, and Learning Algorithms (2003)