I'm implementing a method of sampling from a CTMC from here.

I'm trying to calculate the transition probability matrix but the rows are not summing to 1, except at 0.

I've diagonalised my rate matrix $Q$ to give me a matrix $D$ such that $Q=UDU^{-1}$ where $U$ is the matrix with it's columns the eigenvectors of $Q$ and $D$ the diagonal matrix with diagonal entries the eigenvalues of $Q$.

I should then be able to calculate the transition probability matrix of the CTMC using the equation $$P(t)=e^{Qt}=Ue^{tQ}U^{-1}.$$

I've implemented this using R, with the following code:

`eig <- eigen(rateM) U <- eig$vectors invU <- solve(U) Pt <- U%*%diag(exp(eig$values*t))%*%invU `

where

`> rateM [,1] [,2] [,3] [,4] [1,] -1.1224490 0.6122449 0.3061224 0.2040816 [2,] 0.4081633 -0.9183673 0.3061224 0.2040816 [3,] 0.2040816 0.3061224 -0.9183673 0.2040816 [4,] 0.2040816 0.3061224 0.6122449 -1.1224490 > t [1] 3 `

The rows of the rate matrix all sum to zero, $UU^{-1}=I$ as it should, but the larger t gets, the further from 1 Pt gets. E.g.

`> sum((U%*%diag(exp(eig$values*0))%*%invU)[1,]) [1] 1 > sum((U%*%diag(exp(eig$values*1))%*%invU)[1,]) [1] 0.9773404 > sum((U%*%diag(exp(eig$values*2))%*%invU)[1,]) [1] 0.9316814 > sum((U%*%diag(exp(eig$values*3))%*%invU)[1,]) [1] 0.8799868 > sum((U%*%diag(exp(eig$values*10))%*%invU)[1,]) [1] 0.5701228 `

Is this owing to some sort of numerical error, or am I doing something incorrectly?

**Contents**hide

#### Best Answer

Converting comment to answer, since the comment was on the money, thereby allowing this to be closed. Credit to whuber for his initial comment.

The 3rd row of rateM sums to -0.2040817, not to zero, and is therefore erroneous. Therefore the transition probability matrix will be erroneous.

### Similar Posts:

- Solved – Why is it necessary to fix a matrix diagonal and after this calculate the exponential to assess transition probabilities
- Solved – Creating a probability transition matrix
- Solved – Using the covariance matrix to calculate correlations
- Solved – How to compute robust standard errors of the coefficients in multiple regression
- Solved – Steady state probabilities for a continuous-time Markov chain