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Unformatted text preview: Chapter14 Entropy 141 Introduction Probability of an event A , denoted as ( ) P A , H event A j 3 P uncertainty r measurer If ( ) 1, no unceretainty at all If ( ) 0, P A A P A A = = ( ) 0.5 P A = A b + 3S A P uncertainty r maximum Entropy t 3 experiment S r partition U r uncertainty measure Probability space S P elementary events (outcomes) r Partition { } 1 , , N U A A = L 1 2 N A A A S = L i j A A φ = Entropy of U is defined as 1 1 ( ) log log N N H U p p p p =  L , where ( ) i i p P A = . Information = uncertainty S 3* F F r information b F 3 information Ex. 41 r fairdie r experiment r (a) r { , } U even odd = { } { } 0.5 P even P odd = = Hence 1 1 1 1 ( ) log log log2 2 2 2 2 H U =  = (b) r V P elementary events { } i V f = ( 3 1 { } 6 i P f = ( ) log6 H V = Entropy r log3 r uncertainty about V assuming U (conditional entropy) b F 3 even r oddH uncertainty r log3 Ex. 142 For coin experimentr { } P h p = ( ) log (1 )log(1 ) ( ) H V p p p p h p =  g Fig.142 r ( ) h p verse pP p =0.5 r maximumr p= 0 r 1 r 0r 142 Basic Concepts Let { } { } 1 , , N i U A A A = = L r partition of SP i A events (1) 14 eventsr binary, In this case, we usually denote U by { } , U A A = A is called the complement of A (2) r U P events r elementary events { } i ξ denote it by V and call it element partition (3) A refinement of U is a partition B of S , such that for j B B 2200 j B P i A P subsetr denoted by B U p , r B P U P events 14 events (see Fig.144)r B U p iff j i B A P A common refinement r partitions r refinement (4) The product of U and B is a partitionr elements r i j A B (r )r denoted by U B P U B P P U P B P common refinement Properties V U p for any U U B B U = , ( ) ( ) U B C U B C = If 1 2 3 U U U p p , then 1 3 U U p If B U p , then U B B = Entropy Definition: 1 1 1 ( ) ( log log ) ( ) N N N i i H U p p p p p ϕ = =  + + = L where ( ) i i p P A = ( ) log p p p ϕ =  Q ( ) p ϕ for 1 p P , ∴ ( ) H U P ( ) H U = if r 1 i p = i p = For binary partitionr ( ) p P A = ( ) 1 P A p =  ∴ ( ) log (1 )log(1 ) ( ) H U p p p p h p =  = If 1 1 2 N p p p p = = = = L then 1 1 1 1 ( ) log log log H U N N N N N =  = L In this case, if 2 m N = , then ( ) H U m = . (Here, the base of log operation is 2) Inequalityr ( ) log p p p ϕ =  convex function 1 2 1 2 1 2 ( ) ( ) ( ) ( ) ( ) p p p p p p ϕ ϕ ϕ ϕ ε ϕ ε + < + < + + where 1 1 2 2 p p p p ε ε < + < . t{ ℵ♠ 3 ( ) p ϕ ℵ (see Fig.146) inequality t 3 (1) If { } 1 2 , , , N U A A A = L and { } 2 , , , , a b N B B B A A = L where a B P b B P 1 A split t{ eventsr (see Fig.147) r ( ) ( ) H U H B P , 1 1 2 1 1 2 ( ) ( ) ( ), ( ( )) ( ( )) ( ( )) P A P B P B P A P B P B ϕ ϕ ϕ = + + Q (convex function r ) (2) If B U p , then ( ) ( ) H B H U P . (r generalization of (1)) (3) For U 2200 , ( ) ( ) H U H V P . ( Q V U p ) (4) For U 2200 and B P ( )...
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This note was uploaded on 07/21/2009 for the course CM EM5102 taught by Professor Sinhorngchen during the Fall '08 term at National Chiao Tung University.
 Fall '08
 SinHorngChen

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