Do Markov Chain Transition Matrices Sum by Row or Column?

In summary: Euler's Method both solve for the equilibrium solution of a system of partial differential equations. The algorithm for solving these equations is very similar, but there are a few key differences. First, the Green's Function for a single point is different in each case. Second, the boundary conditions for the two cases are also different. Third, the order of the steps in the algorithm is different. Finally, the solution for a single point is different in each case.In summary, the homework problem asks for the sum of all elements along the rows to sum to 1, whereas Wikipedia says that the sum of all elements along the rows should sum to 1 when we sum all along the row. The problem also asks for the solution
  • #1
Jamin2112
986
12

Homework Statement



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Homework Equations



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The Attempt at a Solution




A few things.

First of all, the homework problem notes that "all the columns should sum to 1," whereas Wikipedia says ∑Pij = 1 when we sum all along the the row i.

Second of all, I don't know where to go after I've constructed my transition matrix. A hint would be much appreciated.
 
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  • #2
Are you having problems with part b?

Basically you are interested in player 10 having a turn on turns 1-5 which means that you are interested in p_(x,5)^(n) where x is the initial state and n is the number of iterations. Based on that, what do you think you need to do (Remember in the homework forum, we can't do your work for you, but give you hints).

With regard to the sum of all elements along the rows being 1 that is correct. An easy way to think about this is that all probabilities in one row are those of disjoint events.

For example the probability p_(0,0) and p(0,1) are disjoint and all the probabilities of p(0,x) where x is any valid state must equal 1 because all possible probabilities starting from 0 and going to something else are considered and there can't be anymore.

Part c is more algorithmic and I'm sure you have the algorithm in your notes. Part d asks you to interpret your results from part c.
 
  • #3
Jamin2112 said:

Homework Statement



screen-capture-1-19.png


screen-capture-2-10.png


Homework Equations



screen-capture-3-16.png


The Attempt at a Solution




A few things.

First of all, the homework problem notes that "all the columns should sum to 1," whereas Wikipedia says ∑Pij = 1 when we sum all along the the row i.

Second of all, I don't know where to go after I've constructed my transition matrix. A hint would be much appreciated.

The vast majority of books and papers use the standard convention in which rows sum to 1. However, I have seen a few papers (mostly from Asian sources) that take the other convention of having columns summing to 1. Basically, one matrix is just the transpose of the other. You should stick to whatever convention your instructor uses, at least when writing up the final solution.

RGV
 

Related to Do Markov Chain Transition Matrices Sum by Row or Column?

1. What is a Markov chain?

A Markov chain is a mathematical model that describes a sequence of events where the probability of each event depends only on the state of the previous event. It is used to model a wide range of processes, such as weather patterns, stock market fluctuations, and even language generation.

2. How does a Markov chain work?

A Markov chain works by defining a state space and a transition matrix. The state space represents all possible states of the system, and the transition matrix shows the probability of moving from one state to another. By repeatedly applying the transition matrix, we can predict the probability of ending up in different states over time.

3. What are some real-world applications of Markov chains?

Markov chains have a wide range of applications in various fields. They are commonly used in finance to model stock prices and market trends. In biology, they are used to study genetic sequences and protein structures. They are also used in natural language processing to generate text and in speech recognition to predict the next word in a sentence.

4. What are the limitations of Markov chains?

Markov chains assume that the probability of transitioning to a new state depends only on the current state, and not on any previous states. This can be a limitation in cases where the probability of transitioning may also depend on past events. Additionally, Markov chains can only model processes that have discrete states and do not take into account any external factors that may influence the system.

5. How can Markov chains be improved or extended?

One way to improve Markov chains is by using higher-order Markov chains, which consider not only the current state but also the previous n states. This can result in more accurate predictions, but it also increases the complexity of the model. Alternatively, some extensions, such as hidden Markov models, incorporate hidden variables that can account for external factors and improve the accuracy of the model.

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