On conditioned probability matrix and channel theory and Marcov chains
Yu Guan · 2000
Regarding all dispersed stochastic variable's distribution as unit space and treating conditioned probability matrixes as a mapping between unit spaces , the author applies these to information theory and obtains a better algorithm of channel capacity , and proves that there must be a steady point if the state of transition probability matrix in Marcov Chains is limited .