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第1行： − 此词条暂由彩云小译翻译，未经人工整理和审校，带来阅读不便，请见谅。{{Information theory}}+ − − + + − − − − 第20行：
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第32行： − − − − == Statements == − − 声明 第55行：
第41行： − 信源编码是从一个信息源(一系列)符号到一系列字母符号(通常是比特)的映射，这样，信源符号可以从二进制比特(无损源编码)中精确地恢复，或者在一定的失真(有损源编码)中恢复。这就是数据压缩的概念。+ − − − − === Source coding theorem === − − 信源编码定理 第72行：
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第59行： − 封锁引用 i.i.i.i.d。每个具有熵的随机变量都可以被压缩成多个位，而信息丢失的风险可以忽略不计; 但反过来，如果它们被压缩成少于位的话，信息几乎肯定会丢失。 / blockquote+ − − − − === Source coding theorem for symbol codes === − − 符号码的信源编码定理 第97行：
第69行： − 设表示两个有限字母，并且设}和}分别表示来自这两个字母的所有有限单词的集合。+ − − 第107行：
第77行： − 假设一个随机变量接受值，并且让它成为一个唯一可解码的代码，从}}到}} ，其中子2 / sub a }}。用码字长度表示随机变量。+ − − 第118行：
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第93行： − 数学 frac { h (x)}{ log 2 a } leq mathbb { e }[ s ] frac { h (x)}{ log 2 a } + 1 / math+ − − 第137行：
第101行： − + − − − − == Proof: Source coding theorem == − − 证明: 信源编码定理 第153行：
第111行： − 特定的是内部识别码。来源，它的时间序列是内部识别码。离散值情形和连续值情形的熵微分熵。信源编码定理指出，对于任何。对于任何大于信号源熵值的速率，都有足够大的编码器，它采用内部识别技术。重复的源，，并映射到二进制位，以便源符号是可恢复的二进制位的概率至少。+ − − 第164行：
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第143行： − 数学 a n ^ varepsilon 左(x1， cdots，xn) : 左 |-frac {1} n } log p (x1， cdots，xn)-h n (x)右 | varepsilon 右。 / math+ − − 第203行：
第151行： − 渐近等同分割特性表明，对于足够大的数据，由源生成的序列位于典型集中的概率，} ，正如定义的接近一样。特别是，对于足够大来说，数学 p ((x1，x2， cdots，xn) / math 可以任意接近于1，特别是，比 math 1- varepsilon / math (参见+ 第210行：
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第213行： − + − − − − == Proof: Source coding theorem for symbol codes == − − 证明: 符号码的源编码定理 第301行：
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第369行： − 因此，由于卡夫公司的不平等性，存在一种无前缀的代码，具有这些单词长度。因此，最小值满足+ − − 第483行：
第377行： − 数学 begin { align }+ 第489行：
第383行： − 文章从理论和实践两个方面论述了当前我国社会主义新农村建设中存在的问题，并提出了相应的对策+ 第495行：
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第407行： − End { align } / math+ − − − − ==Extension to non-stationary independent sources == − − 扩展到非平稳的独立源 − − − − === Fixed Rate lossless source coding for discrete time non-stationary independent sources=== − − 离散时间非平稳独立信源的固定速率无损信源编码 第540行：
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第429行： − 数学 a n ^ varepslon 左 x 1 ^ n: 左 |-frac {1} log p left (x1，cdots，xn right)-overline { h }(x)右 | varepslon right。 数学+ − − 第559行：
第437行： − 然后，对于给定，对于足够大,)1-}。现在我们只是在一个典型的集合中对序列进行编码，通常的信源编码方法表明这个集合的基数小于 math 2 ^ { n (换行{ h }(x) + varepsilon)} / math。因此，在一个平均值上，(x) + }位足够用于编码的概率大于，其中，可以任意小，通过使更大。+ − − − − ==See also== − − 参见 − − − − − − − − − − − − ==References== − − 参考资料 第601行：
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Moved page from wikipedia:en:Shannon's source coding theorem (history)
此词条暂由彩云小译翻译，翻译字数共1005，未经人工整理和审校，带来阅读不便，请见谅。
{{short description|Establishes the limits to possible data compression}}
{{Information theory}}
{{about|the theory of source coding in data compression|the term in computer programming|Source code}}
{{about|the theory of source coding in data compression|the term in computer programming|Source code}}
在信息论中，香农信源编码定理(或无噪声编码定理)建立了可能数据压缩的极限，以及香农熵的操作意义。
在信息论中，香农信源编码定理(或无噪声编码定理)建立了可能数据压缩的极限，以及香农熵的操作意义。
Named after Claude Shannon, the source coding theorem shows that (in the limit, as the length of a stream of independent and identically-distributed random variable (i.i.d.) data tends to infinity) it is impossible to compress the data such that the code rate (average number of bits per symbol) is less than the Shannon entropy of the source, without it being virtually certain that information will be lost. However it is possible to get the code rate arbitrarily close to the Shannon entropy, with negligible probability of loss.
Named after Claude Shannon, the source coding theorem shows that (in the limit, as the length of a stream of independent and identically-distributed random variable (i.i.d.) data tends to infinity) it is impossible to compress the data such that the code rate (average number of bits per symbol) is less than the Shannon entropy of the source, without it being virtually certain that information will be lost. However it is possible to get the code rate arbitrarily close to the Shannon entropy, with negligible probability of loss.
以香农命名的信源编码定理表明(在极限下，作为独立同分布随机变量流的长度)数据趋于无穷大)不可能压缩数据使得码率(每个符号的平均比特数)小于信源的香农熵，事实上不可能确定信息会丢失。然而，这是可能的得到任意接近香农熵的码率，与可以忽略的概率损失。
以香农命名的信源编码定理表明(在极限下，作为独立同分布随机变量(i.i.d)流的长度数据趋于无穷大)不可能压缩数据，使码率(每个符号的平均比特数)小于信源的香农熵，而事实上又不能确定信息会丢失。然而，可以任意地使码率接近香农熵，损失的概率可以忽略不计。
符号码的信源编码定理在最小可能期望码字长度上设置了一个上下界，该上下界是输入字(被视为一个随机变量)熵和目标字母表大小的函数。
符号码的信源编码定理在最小可能期望码字长度上设置了一个上下界，该上下界是输入字(被视为一个随机变量)熵和目标字母表大小的函数。
== Statements ==
== Statements ==
''Source coding'' is a mapping from (a sequence of) symbols from an information [[Information theory#Source theory|source]] to a sequence of alphabet symbols (usually bits) such that the source symbols can be exactly recovered from the binary bits (lossless source coding) or recovered within some distortion (lossy source coding). This is the concept behind [[data compression]].
''Source coding'' is a mapping from (a sequence of) symbols from an information [[Information theory#Source theory|source]] to a sequence of alphabet symbols (usually bits) such that the source symbols can be exactly recovered from the binary bits (lossless source coding) or recovered within some distortion (lossy source coding). This is the concept behind [[data compression]].
Source coding is a mapping from (a sequence of) symbols from an information source to a sequence of alphabet symbols (usually bits) such that the source symbols can be exactly recovered from the binary bits (lossless source coding) or recovered within some distortion (lossy source coding). This is the concept behind data compression.
Source coding is a mapping from (a sequence of) symbols from an information source to a sequence of alphabet symbols (usually bits) such that the source symbols can be exactly recovered from the binary bits (lossless source coding) or recovered within some distortion (lossy source coding). This is the concept behind data compression.
信源编码是从信源符号序列到字母符号序列(通常是比特)的映射，以使信源符号能够准确地从二进制比特位(无损源编码)恢复或在某种失真(有损源编码)内恢复。这就是数据压缩的概念。
=== Source coding theorem ===
=== Source coding theorem ===
In information theory, the '''source coding theorem''' (Shannon 1948)<ref name="Shannon"/> informally states that (MacKay 2003, pg. 81,<ref name="MacKay"/> Cover 2006, Chapter 5<ref name="Cover"/>):
In information theory, the '''source coding theorem''' (Shannon 1948)<ref name="Shannon"/> informally states that (MacKay 2003, pg. 81,<ref name="MacKay"/> Cover 2006, Chapter 5<ref name="Cover"/>):
在信息论中，信源编码定理(Shannon，1948)非正式地指出(MacKay，2003，pg。81,2006年封面，第5章) :
在信息论中，信源编码定理(Shannon，1948)非正式地指出(MacKay，2003，pg。81,2006年封面，第5章) :
<blockquote> i.i.d. random variables each with entropy can be compressed into more than bits with negligible risk of information loss, as ; but conversely, if they are compressed into fewer than bits it is virtually certain that information will be lost.</blockquote>
<blockquote> i.i.d. random variables each with entropy can be compressed into more than bits with negligible risk of information loss, as ; but conversely, if they are compressed into fewer than bits it is virtually certain that information will be lost.</blockquote>
I.i.i.d.随机变量中每一个都有熵，可以压缩成多个比特，信息丢失的风险可以忽略不计，但反过来，如果它们被压缩成比特少于比特，信息几乎肯定会丢失。</blockquote >
=== Source coding theorem for symbol codes ===
=== Source coding theorem for symbol codes ===
Let {{math|Σ<sub>1</sub>, Σ<sub>2</sub>}} denote two finite alphabets and let {{math|Σ{{su|b=1|p=∗}}}} and {{math|Σ{{su|b=2|p=∗}}}} denote the [[Kleene star|set of all finite words]] from those alphabets (respectively).
Let {{math|Σ<sub>1</sub>, Σ<sub>2</sub>}} denote two finite alphabets and let {{math|Σ{{su|b=1|p=∗}}}} and {{math|Σ{{su|b=2|p=∗}}}} denote the [[Kleene star|set of all finite words]] from those alphabets (respectively).
Let denote two finite alphabets and let }} and }} denote the set of all finite words from those alphabets (respectively).
Let denote two finite alphabets and let }} and }} denote the set of all finite words from those alphabets (respectively).
让我们表示两个有限的字母，并且让}和}分别表示来自这两个字母的所有有限单词的集合。
Suppose that is a random variable taking values in and let be a uniquely decodable code from }} to }} where Σ<sub>2</sub> a}}. Let denote the random variable given by the length of codeword .
Suppose that is a random variable taking values in and let be a uniquely decodable code from }} to }} where Σ<sub>2</sub> a}}. Let denote the random variable given by the length of codeword .
假设这是一个随机变量，其中 σ < sub > 2 </sub > a }}是一个唯一可解码的。用码字长度表示随机变量。
如果是最优的，在这个意义上，它有最小的预期字长度，那么(香农1948) :
如果是最优的，在这个意义上，它有最小的预期字长度，那么(香农1948) :
<math> \frac{H(X)}{\log_2 a} \leq \mathbb{E}[S] < \frac{H(X)}{\log_2 a} +1 </math>
<math> \frac{H(X)}{\log_2 a} \leq \mathbb{E}[S] < \frac{H(X)}{\log_2 a} +1 </math>
{ math > frac { h (x)}{ log _ 2a } leq mathbb { e }[ s ] < frac { h (x)}{ log _ 2a } + 1 </math >
Where <math>\mathbb{E}</math> denotes the expected value operator.
Where <math>\mathbb{E}</math> denotes the expected value operator.
其中 math mathbb { e } / math 表示期望值操作符。
其中 < math > mathbb { e } </math > 表示期望值操作符。
== Proof: Source coding theorem ==
== Proof: Source coding theorem ==
Given {{mvar|X}} is an [[independent identically-distributed random variables|i.i.d.]] source, its [[time series]] {{math|''X''<sub>1</sub>, ..., ''X<sub>n</sub>''}} is i.i.d. with [[Entropy_(information_theory)|entropy]] {{math|''H''(''X'')}} in the discrete-valued case and [[differential entropy]] in the continuous-valued case. The Source coding theorem states that for any {{math|''ε'' > 0}}, i.e. for any [[information theory#Rate|rate]] {{math|''H''(''X'') + ''ε''}} larger than the [[entropy]] of the source, there is large enough {{mvar|n}} and an encoder that takes {{mvar|n}} i.i.d. repetition of the source, {{math|''X''<sup>1:''n''</sup>}}, and maps it to {{math|''n''(''H''(''X'') + ''ε'')}} binary bits such that the source symbols {{math|''X''<sup>1:''n''</sup>}} are recoverable from the binary bits with probability of at least {{math|1 − ''ε''}}.
Given {{mvar|X}} is an [[independent identically-distributed random variables|i.i.d.]] source, its [[time series]] {{math|''X''<sub>1</sub>, ..., ''X<sub>n</sub>''}} is i.i.d. with [[Entropy_(information_theory)|entropy]] {{math|''H''(''X'')}} in the discrete-valued case and [[differential entropy]] in the continuous-valued case. The Source coding theorem states that for any {{math|''ε'' > 0}}, i.e. for any [[information theory#Rate|rate]] {{math|''H''(''X'') + ''ε''}} larger than the [[entropy]] of the source, there is large enough {{mvar|n}} and an encoder that takes {{mvar|n}} i.i.d. repetition of the source, {{math|''X''<sup>1:''n''</sup>}}, and maps it to {{math|''n''(''H''(''X'') + ''ε'')}} binary bits such that the source symbols {{math|''X''<sup>1:''n''</sup>}} are recoverable from the binary bits with probability of at least {{math|1 − ''ε''}}.
Given is an i.i.d. source, its time series is i.i.d. with entropy in the discrete-valued case and differential entropy in the continuous-valued case. The Source coding theorem states that for any , i.e. for any rate larger than the entropy of the source, there is large enough and an encoder that takes i.i.d. repetition of the source, , and maps it to binary bits such that the source symbols are recoverable from the binary bits with probability of at least .
Given is an i.i.d. source, its time series is i.i.d. with entropy in the discrete-valued case and differential entropy in the continuous-valued case. The Source coding theorem states that for any , i.e. for any rate larger than the entropy of the source, there is large enough and an encoder that takes i.i.d. repetition of the source, , and maps it to binary bits such that the source symbols are recoverable from the binary bits with probability of at least .
特定的是内部识别码。来源，它的时间序列是内部识别码。离散值情形和连续值情形的熵微分熵。信源编码定理指出，对于任何。对于任何大于信号源熵值的速率，都有足够大的编码器，它采用内部识别技术。重复的源，，并映射到二进制位，以便源符号是可恢复的二进制位的概率至少为。
可达性的证明。修复一些，然后让
可达性的证明。修复一些，然后让
<math>p(x_1, \ldots, x_n) = \Pr \left[X_1 = x_1, \cdots, X_n = x_n \right].</math>
<math>p(x_1, \ldots, x_n) = \Pr \left[X_1 = x_1, \cdots, X_n = x_n \right].</math>
P (x _ 1，点，x _ n) = Pr 左[ x _ 1 = x _ 1，点，x _ n = x _ n 右]
典型的集合，}} ，定义如下:
典型的集合，}} ，定义如下:
<math>A_n^\varepsilon =\left\{(x_1, \cdots, x_n) \ : \ \left|-\frac{1}{n} \log p(x_1, \cdots, x_n) - H_n(X)\right| < \varepsilon \right\}.</math>
<math>A_n^\varepsilon =\left\{(x_1, \cdots, x_n) \ : \ \left|-\frac{1}{n} \log p(x_1, \cdots, x_n) - H_n(X)\right| < \varepsilon \right\}.</math>
[ math > a _ n ^ varepsilon = left {(x _ 1，cdots，x _ n) : left |-frac {1}{ n } log p (x _ 1，cdots，x _ n)-h _ n (x) right | < varepsilon right } . </math >
The Asymptotic Equipartition Property (AEP) shows that for large enough , the probability that a sequence generated by the source lies in the typical set, }}, as defined approaches one. In particular, for sufficiently large , <math>P((X_1,X_2,\cdots,X_n) \in A_n^\varepsilon)</math> can be made arbitrarily close to 1, and specifically, greater than <math>1-\varepsilon</math> (See
The Asymptotic Equipartition Property (AEP) shows that for large enough , the probability that a sequence generated by the source lies in the typical set, }}, as defined approaches one. In particular, for sufficiently large , <math>P((X_1,X_2,\cdots,X_n) \in A_n^\varepsilon)</math> can be made arbitrarily close to 1, and specifically, greater than <math>1-\varepsilon</math> (See
渐近等同分割特性表明，对于足够大的数据，由源生成的序列位于典型集合中的概率，} ，正如定义的接近一样。特别是，对于足够大来说，a _ n ^ varepsilon 中的 p ((x _ 1，x _ 2，点，x _ n) </math > 可以任意接近于1，特别是，比 math > 1-varepsilon </math > 更大
[[Asymptotic equipartition property#AEP for discrete-time i.i.d. sources|AEP]] for a proof).
[[Asymptotic equipartition property#AEP for discrete-time i.i.d. sources|AEP]] for a proof).
为证明 AEP)。
为证明 AEP)。
典型集合的定义意味着处于典型集合中的序列满足:
典型集合的定义意味着处于典型集合中的序列满足:
<math>2^{-n(H(X)+\varepsilon)} \leq p \left (x_1, \cdots, x_n \right ) \leq 2^{-n(H(X)-\varepsilon)}</math>
<math>2^{-n(H(X)+\varepsilon)} \leq p \left (x_1, \cdots, x_n \right ) \leq 2^{-n(H(X)-\varepsilon)}</math>
2 ^ {-n (h (x) + varepsilon)} leq p left (x1， cdots，xn right) leq 2 ^ {-n (h (x)- varepsilon)} / math
2 ^ {-n (h (x) + varepsilon)} leq p left (x _ 1，cdots，x _ n right) leq 2 ^ {-n (h (x)-varepsilon)} </math >
请注意:
请注意:
*The probability of a sequence <math>(X_1,X_2,\cdots X_n)</math> being drawn from {{math|''A''{{su|b=''n''|p=''ε''}}}} is greater than {{math|1 − ''ε''}}.
*The probability of a sequence <math>(X_1,X_2,\cdots X_n)</math> being drawn from {{math|''A''{{su|b=''n''|p=''ε''}}}} is greater than {{math|1 − ''ε''}}.
*<math>\left| A_n^\varepsilon \right| \leq 2^{n(H(X)+\varepsilon)}</math>, which follows from the left hand side (lower bound) for <math> p(x_1,x_2,\cdots x_n)</math>.
*<math>\left| A_n^\varepsilon \right| \leq 2^{n(H(X)+\varepsilon)}</math>, which follows from the left hand side (lower bound) for <math> p(x_1,x_2,\cdots x_n)</math>.
*<math>\left| A_n^\varepsilon \right| \geq (1-\varepsilon) 2^{n(H(X)-\varepsilon)}</math>, which follows from upper bound for <math> p(x_1,x_2,\cdots x_n)</math> and the lower bound on the total probability of the whole set {{math|''A''{{su|b=''n''|p=''ε''}}}}.
*<math>\left| A_n^\varepsilon \right| \geq (1-\varepsilon) 2^{n(H(X)-\varepsilon)}</math>, which follows from upper bound for <math> p(x_1,x_2,\cdots x_n)</math> and the lower bound on the total probability of the whole set {{math|''A''{{su|b=''n''|p=''ε''}}}}.
Since <math>\left| A_n^\varepsilon \right| \leq 2^{n(H(X)+\varepsilon)}, n(H(X)+\varepsilon)</math> bits are enough to point to any string in this set.
Since <math>\left| A_n^\varepsilon \right| \leq 2^{n(H(X)+\varepsilon)}, n(H(X)+\varepsilon)</math> bits are enough to point to any string in this set.
由于 math 左 | n ^ varepsilon right | leq 2 ^ { n (h (x) + varepsilon)} ，n (h (x) + varepsilon) / math 位足以指向该集合中的任何字符串。
由于 < math > 左 | a _ n ^ varepslon 右 | leq 2 ^ { n (h (x) + varepslon)} ，n (h (x) + varepslon) </math > 位足以指向该集合中的任何字符串。
The encoding algorithm: The encoder checks if the input sequence lies within the typical set; if yes, it outputs the index of the input sequence within the typical set; if not, the encoder outputs an arbitrary digit number. As long as the input sequence lies within the typical set (with probability at least ), the encoder doesn't make any error. So, the probability of error of the encoder is bounded above by .
The encoding algorithm: The encoder checks if the input sequence lies within the typical set; if yes, it outputs the index of the input sequence within the typical set; if not, the encoder outputs an arbitrary digit number. As long as the input sequence lies within the typical set (with probability at least ), the encoder doesn't make any error. So, the probability of error of the encoder is bounded above by .
编码算法: 编码器检查输入序列是否在典型集合中; 如果是，它输出典型集合中输入序列的索引; 如果不是，编码器输出任意数字号。只要输入序列位于典型集内(至少概率) ，编码器就不会出错。因此，该编码器的错误概率是以上有界的。
编码算法: 编码器检查输入序列是否在典型集合中; 如果是，它输出典型集合中输入序列的索引; 如果不是，编码器输出任意数字号。只要输入序列位于典型集(至少概率)内，编码器就不会出错。因此，该编码器的错误概率是以上有界的。
Proof of Converse. The converse is proved by showing that any set of size smaller than }} (in the sense of exponent) would cover a set of probability bounded away from .
Proof of Converse. The converse is proved by showing that any set of size smaller than }} (in the sense of exponent) would cover a set of probability bounded away from .
逆向的证明。通过证明任意一个小于}(指数意义下)的集合可以覆盖一个距离有界的概率集合，从而证明了逆向的结论。
逆向的证明。通过证明任意一个小于}(指数意义下)的集合可以覆盖一个远离有界的概率集合，从而证明了逆向的结论。
== Proof: Source coding theorem for symbol codes ==
== Proof: Source coding theorem for symbol codes ==
For {{math|1 ≤ ''i'' ≤ ''n''}} let {{math|''s<sub>i</sub>''}} denote the word length of each possible {{math|''x<sub>i</sub>''}}. Define <math>q_i = a^{-s_i}/C</math>, where {{mvar|C}} is chosen so that {{math|''q''<sub>1</sub> + ... + ''q<sub>n</sub>'' {{=}} 1}}. Then
For {{math|1 ≤ ''i'' ≤ ''n''}} let {{math|''s<sub>i</sub>''}} denote the word length of each possible {{math|''x<sub>i</sub>''}}. Define <math>q_i = a^{-s_i}/C</math>, where {{mvar|C}} is chosen so that {{math|''q''<sub>1</sub> + ... + ''q<sub>n</sub>'' {{=}} 1}}. Then
For let denote the word length of each possible . Define <math>q_i = a^{-s_i}/C</math>, where is chosen so that 1}}. Then
For let denote the word length of each possible . Define <math>q_i = a^{-s_i}/C</math>, where is chosen so that 1}}. Then
因为 let 表示每个可能的单词的长度。定义 math q i a ^ {-s i } / c / math，在其中选择1}。然后
因为 let 表示每个可能的单词的长度。定义 < math > q _ i = a ^ {-s _ i }/c </math > ，在哪里选择以便1}}。然后
<math>\begin{align}
<math>\begin{align}
1.1.1.2.2.2.2.2.2.3.3.3.3.3.3.3.3.3.3.3.3.3.3.3.3.3.3.3.3.3.3.3.3.3.3.3.3.3.3.3.3.3.3.3.3.3.3.3.3.3.4.3.3.3.3.3.3.3.3.3.3.3.4.3.3.3.3.3.3.3.3.3
H(X) &= -\sum_{i=1}^n p_i \log_2 p_i \\
H(X) &= -\sum_{i=1}^n p_i \log_2 p_i \\
H(X) &= -\sum_{i=1}^n p_i \log_2 p_i \\
H(X) &= -\sum_{i=1}^n p_i \log_2 p_i \\
H (x) &-sum { i } ^ n pi log 2pi
H (x) & =-sum _ { i = 1} ^ n p _ i log _ 2 p _ i
&\leq -\sum_{i=1}^n p_i \log_2 q_i \\
&\leq -\sum_{i=1}^n p_i \log_2 q_i \\
&= -\sum_{i=1}^n p_i \log_2 a^{-s_i} + \sum_{i=1}^n p_i \log_2 C \\
&= -\sum_{i=1}^n p_i \log_2 a^{-s_i} + \sum_{i=1}^n p_i \log_2 C \\
2 a ^ {-s i } + sum { i } ^ n p i log 2 c
& =-sum { i = 1} ^ n p _ i log _ 2 a ^ {-s _ i } + sum _ { i = 1} ^ n p _ i log _ 2 c
&= -\sum_{i=1}^n p_i \log_2 a^{-s_i} + \log_2 C \\
&= -\sum_{i=1}^n p_i \log_2 a^{-s_i} + \log_2 C \\
&= -\sum_{i=1}^n p_i \log_2 a^{-s_i} + \log_2 C \\
&= -\sum_{i=1}^n p_i \log_2 a^{-s_i} + \log_2 C \\
2 a ^ {-s i } + log 2 c
& =-sum { i = 1} ^ n p _ i log _ 2 a ^ {-s _ i } + log _ 2 c
&\leq -\sum_{i=1}^n - s_i p_i \log_2 a \\
&\leq -\sum_{i=1}^n - s_i p_i \log_2 a \\
&\leq \mathbb{E} S \log_2 a \\
&\leq \mathbb{E} S \log_2 a \\
2 a
\end{align}</math>
\end{align}</math>
\end{align}</math>
\end{align}</math>
结束{ align } </math >
第二行来自吉布斯的不平等，第五行来自卡夫的不平等:
第二行来自吉布斯的不平等，第五行来自卡夫的不平等:
<math>C = \sum_{i=1}^n a^{-s_i} \leq 1</math>
<math>C = \sum_{i=1}^n a^{-s_i} \leq 1</math>
数学 c sum { i } ^ n a ^ {-s i } leq 1 / math
[数学] c = sum { i = 1} ^ n a ^ {-s _ i } leq 1
所以。
所以。
对于第二个不等式，我们可以设定
对于第二个不等式，我们可以设定
<math>s_i = \lceil - \log_a p_i \rceil </math>
<math>s_i = \lceil - \log_a p_i \rceil </math>
数学-数学-数学
[数学][数学]
所以
所以
<math> - \log_a p_i \leq s_i < -\log_a p_i + 1 </math>
<math> - \log_a p_i \leq s_i < -\log_a p_i + 1 </math>
[数学]-log _ a p _ i leq s _ i <-log _ a p _ i + 1
所以
所以
<math> a^{-s_i} \leq p_i</math>
<math> a^{-s_i} \leq p_i</math>
[ math ] a ^ {-s _ i } leq p _ i
及
及
<math> \sum a^{-s_i} \leq \sum p_i = 1</math>
<math> \sum a^{-s_i} \leq \sum p_i = 1</math>
[ math ] sum a ^ {-s _ i } leq sum p _ i = 1 </math >
and so by Kraft's inequality there exists a prefix-free code having those word lengths. Thus the minimal satisfies
and so by Kraft's inequality there exists a prefix-free code having those word lengths. Thus the minimal satisfies
因此，根据卡夫的不平等性，存在一种无前缀的代码，其单词长度与前缀无关。因此，最小值满足
<math>\begin{align}
<math>\begin{align}
1.1.1.2.2.2.2.2.2.3.3.3.3.3.3.3.3.3.3.3.3.3.3.3.3.3.3.3.3.3.3.3.3.3.3.3.3.3.3.3.3.3.3.3.3.3.3.3.3.3.4.3.3.3.3.3.3.3.3.3.3.3.4.3.3.3.3.3.3.3.3.3
\mathbb{E} S & = \sum p_i s_i \\
\mathbb{E} S & = \sum p_i s_i \\
\mathbb{E} S & = \sum p_i s_i \\
\mathbb{E} S & = \sum p_i s_i \\
数学家们正在研究这个问题
& < \sum p_i \left( -\log_a p_i +1 \right) \\
& < \sum p_i \left( -\log_a p_i +1 \right) \\
& < \sum p_i \left( -\log_a p_i +1 \right) \\
& < \sum p_i \left( -\log_a p_i +1 \right) \\
(- log a pi + 1右)
& < sum p _ i left (- log _ a p _ i + 1 right)
& = \sum - p_i \frac{\log_2 p_i}{\log_2 a} +1 \\
& = \sum - p_i \frac{\log_2 p_i}{\log_2 a} +1 \\
& = \sum - p_i \frac{\log_2 p_i}{\log_2 a} +1 \\
& = \sum - p_i \frac{\log_2 p_i}{\log_2 a} +1 \\
和 = sum-p _ i frac { log _ 2 p _ i }{ log _ 2 a } + 1
& = \frac{H(X)}{\log_2 a} +1 \\
& = \frac{H(X)}{\log_2 a} +1 \\
& = \frac{H(X)}{\log_2 a} +1 \\
& = \frac{H(X)}{\log_2 a} +1 \\
& frac { h (x)}{ log 2 a } + 1
& = frac { h (x)}{ log _ 2 a } + 1
\end{align}</math>
\end{align}</math>
\end{align}</math>
\end{align}</math>
结束{ align } </math >
==Extension to non-stationary independent sources ==
==Extension to non-stationary independent sources ==
=== Fixed Rate lossless source coding for discrete time non-stationary independent sources===
=== Fixed Rate lossless source coding for discrete time non-stationary independent sources===
Define typical set {{math|''A''{{su|b=''n''|p=''ε''}}}} as:
Define typical set {{math|''A''{{su|b=''n''|p=''ε''}}}} as:
定义典型的集合}}如下:
定义典型的集合}}如下:
<math>A_n^\varepsilon = \left \{x_1^n \ : \ \left|-\frac{1}{n} \log p \left (X_1, \cdots, X_n \right ) - \overline{H_n}(X)\right| < \varepsilon \right \}.</math>
<math>A_n^\varepsilon = \left \{x_1^n \ : \ \left|-\frac{1}{n} \log p \left (X_1, \cdots, X_n \right ) - \overline{H_n}(X)\right| < \varepsilon \right \}.</math>
< math > a _ n ^ varepsilon = left { x _ 1 ^ n: left |-frac {1}{ n } log p left (x _ 1，cdots，x _ n right)-overline { h _ n }(x) right | < varepsilon right }.数学
Then, for given , for large enough, ) > 1 − δ}}. Now we just encode the sequences in the typical set, and usual methods in source coding show that the cardinality of this set is smaller than <math>2^{n(\overline{H_n}(X)+\varepsilon)}</math>. Thus, on an average, (X) + ε}} bits suffice for encoding with probability greater than , where and can be made arbitrarily small, by making larger.
Then, for given , for large enough, ) > 1 − δ}}. Now we just encode the sequences in the typical set, and usual methods in source coding show that the cardinality of this set is smaller than <math>2^{n(\overline{H_n}(X)+\varepsilon)}</math>. Thus, on an average, (X) + ε}} bits suffice for encoding with probability greater than , where and can be made arbitrarily small, by making larger.
然后，对于给定的，对于足够大的,) > 1-δ }}。现在我们只对序列进行典型集合编码，通常的信源编码方法表明，这个集合的基数小于 < math > 2 ^ { n (overline { h _ n }(x) + varepsilon)} </math > 。因此，在一个平均值上，(x) + ε }位足够用于编码的概率大于，其中，可以任意小，通过使更大。
==See also==
==See also==
* [[Channel coding]]
* [[Channel coding]]
* [[noisy channel coding theorem|Noisy Channel Coding Theorem]]
* [[noisy channel coding theorem|Noisy Channel Coding Theorem]]
* [[Error exponent]]
* [[Error exponent]]
* [[Asymptotic equipartition property|Asymptotic Equipartition Property]] (AEP)
* [[Asymptotic equipartition property|Asymptotic Equipartition Property]] (AEP)
==References==
==References==
{{Reflist|refs=
{{Reflist|refs=
{{Reflist|refs=
{{Reflist|refs=
{通货再膨胀 | 参考文献
{通货再膨胀 | 参考文献 =
<ref name="Shannon">[[C.E. Shannon]], "[http://plan9.bell-labs.com/cm/ms/what/shannonday/shannon1948.pdf A Mathematical Theory of Communication]", ''[[Bell System Technical Journal]]'', vol. 27, pp. 379–423, 623-656, July, October, 1948</ref>
<ref name="Shannon">[[C.E. Shannon]], "[http://plan9.bell-labs.com/cm/ms/what/shannonday/shannon1948.pdf A Mathematical Theory of Communication]", ''[[Bell System Technical Journal]]'', vol. 27, pp. 379–423, 623-656, July, October, 1948</ref>
<ref name="MacKay">David J. C. MacKay. ''[http://www.inference.phy.cam.ac.uk/mackay/itila/book.html Information Theory, Inference, and Learning Algorithms]'' Cambridge: Cambridge University Press, 2003. {{ISBN|0-521-64298-1}}</ref>
<ref name="MacKay">David J. C. MacKay. ''[http://www.inference.phy.cam.ac.uk/mackay/itila/book.html Information Theory, Inference, and Learning Algorithms]'' Cambridge: Cambridge University Press, 2003. {{ISBN|0-521-64298-1}}</ref>
<ref name="Cover">{{cite book | last = Cover | first = Thomas M. | title = Elements of Information Theory | chapter = Chapter 5: Data Compression | year = 2006 | publisher = John Wiley & Sons | isbn = 0-471-24195-4 }}</ref>}}
<ref name="Cover">{{cite book | last = Cover | first = Thomas M. | title = Elements of Information Theory | chapter = Chapter 5: Data Compression | year = 2006 | publisher = John Wiley & Sons | isbn = 0-471-24195-4 }}</ref>}}
}}
}}