Exponential of (Markov Chain) Transition matrix

In summary, the conversation discusses the relationship between a markov chain transition matrix X and its generator Y, where Y is the continuously compounded transition matrix. The conversation also mentions that both X and Y are matrices, and that the sums of Y must sum to 0. The conversation then delves into the topic of raising a matrix to a non-integer power, and the use of eigenvalues and eigenvectors in this process. In conclusion, the conversation provides insights on the applications of taking exponentials or logs of matrices.
  • #1
NewStudent200
5
0
Hi,

I have a (markov chain) transition matrix X which I understand. In particular each row of this matrix sums to 1.

I have used this transition matrix to construct it's generator, Y. I.e. Y is the continuously compounded transition matrix,

X = exp(Y)
X*X = exp(2Y), etc

both X and Y are matrices.

I am told that the sums of Y must sum to 0, but I can not see why this should be the case. Is it obvious?

Many Thanks.
 
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  • #2
Possible hint maybe? [itex]e^0=1[/itex]
 
  • #3
Thanks.

But I have trouble visualizing this for a matrix. Is there aproof somewhere, or a text that you can recommend which gives examples and talks about the applications of taking exponentials or logs of matrices?

Many thanks,
 
  • #4
It is a http://en.wikipedia.org/wiki/Matrix_exponential" right ? If you write down the power series for it you will get a pattern.

[tex]
X = I + Y + \frac{Y^2}{2!} + \ldots
[/tex]
Now if you sum up the rows of X it is 1. On the right hand side you already get 1 from the identity matrix. So all contributions from the remaining terms must be zero right? So I will let you think if your condition is sufficient or necessary.
 
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  • #5
Cool. Thanks a lot!

Thinking about matrices a little further. How does one raise a matrix to a decimal power. I know that in the case of an integer power:

X^n = S.M^n.S^-1

where S is the eigen vector matrix and M is the matrix with eigen values along the diagonal. Now if n is non integer, then does this still hold? Could we also do it via:

Y = X^n
ln(Y) = n.ln(X)
Y = exp(n.ln(X))?

much appreciated.
 
  • #6
NewStudent200 said:
Cool. Thanks a lot!

Thinking about matrices a little further. How does one raise a matrix to a decimal power. I know that in the case of an integer power:

X^n = S.M^n.S^-1

where S is the eigen vector matrix and M is the matrix with eigen values along the diagonal. Now if n is non integer, then does this still hold? Could we also do it via:

Y = X^n
ln(Y) = n.ln(X)
Y = exp(n.ln(X))?

much appreciated.

yes, yes

also:
if [tex] X = S M S^{-1}[/tex] then [tex] e^X = S e^M S^{-1}[/tex], [tex] \log(X) = S \log(M) S^{-1}[/tex], etc.
 

Related to Exponential of (Markov Chain) Transition matrix

What is an exponential of a Markov Chain transition matrix?

The exponential of a Markov Chain transition matrix is a mathematical operation that calculates the probability of transitioning from one state to another after a specified number of steps. It is useful for understanding the long-term behavior of a Markov Chain and predicting future states.

How is the exponential of a Markov Chain transition matrix calculated?

The exponential of a Markov Chain transition matrix is calculated using matrix multiplication. The transition matrix is raised to a power equal to the number of steps, and the resulting matrix represents the probability of transitioning from one state to another after that number of steps.

What does the exponential of a Markov Chain transition matrix tell us?

The exponential of a Markov Chain transition matrix tells us the long-term behavior of the Markov Chain. It can reveal patterns and trends in the data, and can be used to make predictions about future states of the system.

What is the significance of the eigenvalues and eigenvectors in the exponential of a Markov Chain transition matrix?

The eigenvalues and eigenvectors of the exponential of a Markov Chain transition matrix provide important information about the stability and behavior of the system. The dominant eigenvalue (largest in absolute value) corresponds to the long-term behavior of the Markov Chain, while the eigenvector associated with it represents the stationary distribution of the system.

What are some practical applications of the exponential of a Markov Chain transition matrix?

The exponential of a Markov Chain transition matrix has many practical applications in various fields, such as finance, biology, and computer science. It can be used to model and predict stock market trends, analyze DNA sequences, and simulate the behavior of computer algorithms. It is also used in machine learning and artificial intelligence to make predictions and decisions based on data.

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