How Can a Transition Matrix Predict Plant Color Proportions Over Time?

In summary, the conversation discusses the creation of a transition matrix for a breeder's crop of red, pink, and white flowers. The matrix is used to determine the proportions of each color in the crop for the next year. It is also mentioned that red flowers are the most popular at florists and the initial state vector is given as r0 = 1/2, p0 = 1/4, and w0 = 1/4. The conversation also includes a question about writing the initial state vector as a linear combination of eigenvectors. The solution involves finding the eigenvalues and associated eigenvectors of the matrix. The final solution for the linear combination is r(t)= 1/2 + j+k
  • #1
krisrai
15
0

Homework Statement



1. Initially, we assume the breeder’s plants are all growing in a field where they will be
cross-polenated randomly, with genes that can come from anywhere (even neighbouring
fields of flowers). A given plant would thus be crossed randomly, so that its offspring
would get an R or W gene with equal probability. As a result the offspring of the plants
will be as follows:

the offspring of the red plants will be 50% red, 50% pink and 0% white
the offspring of the pink plants will be 25% red, 50% pink and 25% white
the offspring of the white plants will be 0% red, 50% pink and 50% white


Create a transition matrix A so that, given the state vector ~St at time t, the fraction of
the breeder’s crop that is of each color the next year (time t + 1) can be found as
St+1 = A*St
where
St= [rt+1 pt+1 wt+1]^T
Use the probabilities above to populate the columns of the matrix.

2. It turns out that red flowers are the most popular at the florists, so the breeder begins
with an initial state vector, S0, with r0 = 1/2, p0 = 1/4, and w0 = 1/4. Using your matrixfrom question 1, determine the proportions of each type of flower in years t = 1, and t = 2.


3. Write the initial state vector 2 as linear combinations of the eigenvectors.


Homework Equations


LinearMultiplication...


The Attempt at a Solution



1. A=
[1/2 1/4 0
1/2 1/2 1/2
0 1/4 1/2]

2. S(t+1)=AS(t)

so to find S(3) I first need my S(2) and S(1)
which I found:

t=0, S(1)= A*S(0)

[1/2 1/4 0 [1/2 [5/16
1/2 1/2 1/2 X 1/4 = 1/2
0 1/4 1/2] 1/4] 3/16]
*these I know are correct because the proportions add up to 1 or 100%


and t=1 S(2)=AS(1)

[1/2 1/4 0 [5/16 [9/32
1/2 1/2 1/2 X 1/2 = 1/2
0 1/4 1/2] 3/16] 11/32]

*Could someone please tell me where I am going wrong? My proportions of each colour do not add up to one


so for my t=2 S(3)=AS(2) I am scared to do because my flowers are disproportional



3. So here I found my eigenvalues and associated eigenvectors for my matrix A
Lambda1=1
X1=
[1
2
1]

Lambda2=1/2
X2=
[1
0
-1]

Lambda3= 0
X3=
[1
-2
1]

would I be able to write my linear combination of eigen vectors as:
r(t)= 1/2 + j+k+m
p(t)= 1/4 +2j -2m
w(t)= 1/4 +j-k+m

Help would be really appreciated. I need to be able to find my S(10) from this linear equation and I don't know how to do it.
 
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  • #2
krisrai said:

Homework Statement



1. Initially, we assume the breeder’s plants are all growing in a field where they will be
cross-polenated randomly, with genes that can come from anywhere (even neighbouring
fields of flowers). A given plant would thus be crossed randomly, so that its offspring
would get an R or W gene with equal probability. As a result the offspring of the plants
will be as follows:

the offspring of the red plants will be 50% red, 50% pink and 0% white
the offspring of the pink plants will be 25% red, 50% pink and 25% white
the offspring of the white plants will be 0% red, 50% pink and 50% white


Create a transition matrix A so that, given the state vector ~St at time t, the fraction of
the breeder’s crop that is of each color the next year (time t + 1) can be found as
St+1 = A*St
where
St= [rt+1 pt+1 wt+1]^T
Use the probabilities above to populate the columns of the matrix.

2. It turns out that red flowers are the most popular at the florists, so the breeder begins
with an initial state vector, S0, with r0 = 1/2, p0 = 1/4, and w0 = 1/4. Using your matrixfrom question 1, determine the proportions of each type of flower in years t = 1, and t = 2.


3. Write the initial state vector 2 as linear combinations of the eigenvectors.


Homework Equations


LinearMultiplication...


The Attempt at a Solution



1. A=
[1/2 1/4 0
1/2 1/2 1/2
0 1/4 1/2]

2. S(t+1)=AS(t)

so to find S(3) I first need my S(2) and S(1)
which I found:

t=0, S(1)= A*S(0)

[1/2 1/4 0 [1/2 [5/16
1/2 1/2 1/2 X 1/4 = 1/2
0 1/4 1/2] 1/4] 3/16]
*these I know are correct because the proportions add up to 1 or 100%


and t=1 S(2)=AS(1)

[1/2 1/4 0 [5/16 [9/32
1/2 1/2 1/2 X 1/2 = 1/2
0 1/4 1/2] 3/16] 11/32]

*Could someone please tell me where I am going wrong? My proportions of each colour do not add up to one
(1/4)(1/2)+ (1/2)(3/16)= 1/8+ 3/32= ? (NOT 11/32!)

so for my t=2 S(3)=AS(2) I am scared to do because my flowers are disproportional



3. So here I found my eigenvalues and associated eigenvectors for my matrix A
Lambda1=1
X1=
[1
2
1]

Lambda2=1/2
X2=
[1
0
-1]

Lambda3= 0
X3=
[1
-2
1]

would I be able to write my linear combination of eigen vectors as:
r(t)= 1/2 + j+k+m
p(t)= 1/4 +2j -2m
w(t)= 1/4 +j-k+m

Help would be really appreciated. I need to be able to find my S(10) from this linear equation and I don't know how to do it.
 
  • #3
thank you i must have multiplied the other lines wrong much:)
i figured out number 3 this thread is SOLVED
 

FAQ: How Can a Transition Matrix Predict Plant Color Proportions Over Time?

What is a linear combination of eigenvectors?

A linear combination of eigenvectors is a mathematical expression that combines two or more eigenvectors by multiplying each of them by a scalar constant and adding them together. The resulting vector is also an eigenvector of the same matrix.

Why is linear combination of eigenvectors important?

Linear combination of eigenvectors is important because it allows us to find new eigenvectors for a given matrix, which can be used to simplify complex calculations in various fields of science and engineering, such as quantum mechanics, signal processing, and data analysis.

How do you find the coefficients for a linear combination of eigenvectors?

The coefficients for a linear combination of eigenvectors can be found by solving a system of equations, where the coefficients are the unknown variables. Each equation is derived from the condition that the resulting vector must be an eigenvector of the given matrix.

Can a linear combination of eigenvectors result in a non-eigenvector?

No, a linear combination of eigenvectors will always result in another eigenvector of the same matrix. This is because eigenvectors are closed under linear combinations.

How is a linear combination of eigenvectors used in real-world applications?

Linear combination of eigenvectors is used in various real-world applications, such as image and signal processing, data compression, and machine learning. It is also used in physics to describe the behavior of quantum systems and in chemistry to understand the properties of molecules.

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