Python y R: una perspectiva markoviana
Hoy he visto aquí y he escrito
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m <- matrix(c(74, 15, 10, 1, 11, 50, 38, 1, 5, 4, 90, 1, 17, 4, 19, 60), 4, 4, byrow = TRUE) m <- m / 100 |
luego
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m %*% m %*% m %*% m %*% m %*% m %*% m %*% m %*% m %*% m %*% m %*% m %*% m%*% m%*% m%*% m%*% m%*% m%*% m %*% m %*% m %*% m %*% m %*% m %*% m %*% m %*% m %*% m %*% m %*% m %*% m%*% m%*% m%*% m%*% m%*% m%*% m # [,1] [,2] [,3] [,4] #[1,] 0.1926676 0.1133218 0.6696203 0.02439024 #[2,] 0.1926647 0.1133206 0.6696245 0.02439024 #[3,] 0.1926638 0.1133202 0.6696258 0.02439024 #[4,] 0.1926675 0.1133218 0.6696205 0.02439025 |
y finalmente
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res <- eigen(t(m)) res$vectors[,1] / sum(res$vectors[,1]) #[1] 0.19266473 0.11332059 0.66962444 0.02439024 |
Aquí dice por qué.