How to Find Transition Matrices and Vectors in Ordered Bases

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In summary, this conversation discusses the use of ordered bases B and C for R^2, and a transition matrix P from B to C. The conversation includes finding the transition matrix from C to B, determining the vectors w(1) and w(2), and using matrix P to find [u]c and the vector u.
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Homework Statement



Consider ordered bases B={(1,2),(1,1)} and C={w(1),w(2)} for R^2. Suppose P, a 2 X 2 matrix, P=((2,1),(5,3)) (2,1) and (5,3) being vectors in matrix P. P is the transition matrix from B to C.

a)Find the transition matrix from C to B
b) Find w(1) and w(2)
c) if b =(1,2)((1,2) is a vector.) , read as with subscript B, use P to find c also read as with subscript c. Also find vector u.

Homework Equations





The Attempt at a Solution



a) Is just taking the inverse of P. P^1-= ((3,-1),(-5,2))
b) w(1)=2*v(1)+v(2)=2*(1,2)+(1,1)=(3,5)
w(2)= 5*v(1)+3*v(2)=5*(1,2)+3*(1,2)=(8,13). v(1) and v(2) are vectors in ordered base B by the way.
c) b=(1,2)=> v(1)+2*v(2)=(3,4)=u. Not sure how to find c , but I think c = P^1-
 
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No one has replied yet. Are the symbols I used to represent my matrices readable.
 

What is a transitional matrix?

A transitional matrix, also known as a Markov matrix, is a square matrix that represents the probability of transitioning from one state to another in a system. It is commonly used in mathematics and statistics to model and analyze various systems, such as biological populations, economic markets, and weather patterns.

How is a transitional matrix created?

A transitional matrix is created by first identifying the different states of a system and then determining the probabilities of transitioning from one state to another. These probabilities are then organized into a square matrix, with the rows representing the current state and the columns representing the next state.

What is the purpose of using a transitional matrix?

The purpose of using a transitional matrix is to analyze the behavior and predict the future states of a system. By understanding the probabilities of transitioning between different states, we can make informed decisions and optimize the outcomes of a system.

What are the limitations of transitional matrices?

One limitation of transitional matrices is that they assume the system is in a steady state, meaning that the probabilities of transitioning between states do not change over time. This may not be true for all systems, as some may have changing probabilities that are influenced by external factors.

How is a transitional matrix different from a normal matrix?

A transitional matrix is different from a normal matrix in that it represents probabilities rather than values. In a transitional matrix, the rows and columns add up to 1, as they represent the complete set of probabilities for transitioning from one state to another. Normal matrices, on the other hand, may contain any values and do not necessarily add up to 1.

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