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that is, the codeword <math>y</math> that maximizes the probability that <math>x</math> was received, [[conditional probability|given that]] <math>y</math> was sent. If all codewords are equally likely to be sent then this scheme is equivalent to ideal observer decoding.
In fact, by [[Bayes'
:<math>
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The simplest form is due to Prange: Let <math>G</math> be the <math>k \times n</math> generator matrix of <math>C</math> used for encoding. Select <math>k</math> columns of <math>G</math> at random, and denote by <math>G'</math> the corresponding submatrix of <math>G</math>. With reasonable probability <math>G'</math> will have full rank, which means that if we let <math>c'</math> be the sub-vector for the corresponding positions of any codeword <math>c = mG</math> of <math>C</math> for a message <math>m</math>, we can recover <math>m</math> as <math>m = c' G'^{-1}</math>. Hence, if we were lucky that these <math>k</math> positions of the received word <math>y</math> contained no errors, and hence equalled the positions of the sent codeword, then we may decode.
If <math>t</math> errors occurred, the probability of such a fortunate selection of columns is given by <math>\textstyle\binom{n-t}{k}/\binom{n}{k}\approx \exp(-tk/n)</math>.
This method has been improved in various ways, e.g. by Stern<ref name="Stern_1989"/> and [[Anne Canteaut|Canteaut]] and Sendrier.<ref name="Ohta_1998"/>
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