A transition probability matrix $\mathbf{P}$ is said to be doubly stochastic if the sum over each column equals one

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In summary, a transition probability matrix is a mathematical representation of the probabilities of transitioning from one state to another in a system. It is commonly used in Markov chain analysis and can be represented as a square matrix. A doubly stochastic transition probability matrix is one in which the sum of the entries in each column equals one, ensuring that the system will always transition to another state and maintain equilibrium. This is important for accurately modeling and predicting the behavior of a system over time. A regular transition probability matrix does not have this restriction and can potentially lead to the system not being in a state of equilibrium. A transition probability matrix cannot be both doubly stochastic and non-stochastic at the same time.
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Chris L T521
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Here's this week's problem.

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Problem: A transition probability matrix $\mathbf{P}$ is said to be doubly stochastic if the sum over each column equals one; that is,\[\sum_i P_{i,j}=1,\qquad\forall j.\]
If such a chain is irreducible and aperiodic and consists of $M+1$ states $0,1,\ldots,M$, show that the limiting probabilities are given by
\[\pi_j=\frac{1}{M+1},\quad j=0,1,\ldots,M.\]

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No one answered this week's question. You can find my solution below.

To show that this is true, we show that $\pi_j=\frac{1}{M+1}$ satisfies the system of equations $\pi_j=\sum\limits_{i=0}^M\pi_iP_{ij}$ and $\sum\limits_{j=0}^M\pi_j=1$. Supposing that $\pi_j=\frac{1}{M+1}$, we see that\[\sum\limits_{j=0}^M\pi_j=\frac{1}{M+1}\sum\limits_{j=0}^M1=\frac{1}{M+1}(M+1)=1\]
and
\[\pi_j=\sum\limits_{i=0}^M\pi_iP_{ij}\implies \sum\limits_{j=0}^M\sum\limits_{i=0}^M\pi_jP_{ij}=(M+1)\pi=1.\]
Thus, $\pi_j$ must be $\frac{1}{M+1}$ for these equations to be satisfied.
 

FAQ: A transition probability matrix $\mathbf{P}$ is said to be doubly stochastic if the sum over each column equals one

What is a transition probability matrix?

A transition probability matrix is a mathematical representation of the probabilities of transitioning from one state to another in a system. It is commonly used in Markov chain analysis and can be represented as a square matrix with entries representing the probabilities of transitioning from one state to another.

What does it mean for a transition probability matrix to be doubly stochastic?

A transition probability matrix is said to be doubly stochastic if the sum of the entries in each column equals one. This means that the total probability of transitioning to any state from the current state is equal to one, ensuring that the system will always transition to another state.

Why is it important for a transition probability matrix to be doubly stochastic?

A doubly stochastic transition probability matrix ensures that the system is in a state of equilibrium, as the total probability of transitioning to any state from the current state is equal to one. This is an important property for accurately modeling and predicting the behavior of a system over time.

How is a doubly stochastic transition probability matrix different from a regular transition probability matrix?

A regular transition probability matrix does not have the restriction of the sum of the entries in each column equaling one. This means that in a regular matrix, the system may not be in a state of equilibrium and could potentially transition to a state with a lower probability. A doubly stochastic matrix ensures that the system will always transition to another state, maintaining equilibrium.

Can a transition probability matrix be both doubly stochastic and non-stochastic?

No, a transition probability matrix can only be either doubly stochastic or non-stochastic. A doubly stochastic matrix ensures that the system is in a state of equilibrium, while a non-stochastic matrix does not have this property and can potentially lead to the system not transitioning to another state. It is not possible for a matrix to have both properties at the same time.

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