Enthropia

Содержание и основные понятия

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Alterna chapter 27
Enthropia
Alphabetic approach · Hartley · Shannon · Information measure
Chapter 27
quantification of information
===Sennon entropy===Two approaches (alphabetic and substantive) and entropia as a universal measure of uncertainty.
GEF
PIF
OGE No. 1
EEG No. 4.8.11
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Theory
27.1
Alphabetical approach
The power of the alphabet, the weight of the symbol, the volume of the message.
27.2
Contentary approach
Information as a reduction of uncertainty. Hartley formula: I = log2N
27.3
Shannon entropy
H = -· pi·log2pi. Entropy properties, U-creative, connection to coding
27.4
Combinatory of words
Rule of work, placement, word with limitations - EGE type 8
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Practice
27.5
EEG/EGL workshop
10 random tasks from the bank 100, with filters and reviews
27.6
Polygon examination
Quest 800 XP Test 10 questions
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Relationship with other chapters
Previous chapter
Chapter 26 Fano condition
- Prefix codes, Huffman's algorithm, and the average code length is going for Shannon entropy.
Next chapter
Chapter 28 NRF and NFP
- normal forms of logical functions, Carno maps, minimization.
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Key formulas
Concept
Formula
Description
Symbol Weight
i = log₂N
Battle on symbol in power alphabet N
Public service announcements
I = K × i
K symbols x bit to symbol
Hartley.
I = log₂N
Information in selecting 1 of the N equally likely
Shannon entropy
H = −Σ pᵢ log₂(pᵢ)
Source uncertainty measure
Max entropy
H_max = log₂N
With even distribution