Markov Chains and absorption probabilites

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In summary, The conversation discusses a question about a single-celled organism containing particles of type A and B. It mentions that the cell is in a state if it contains a certain number of particles and how daughter cells are formed through cell division. The person is struggling to find the absorption probabilities and expected times to absorption for the case N=3. They mention that they have identified the absorbing states but are unsure how to calculate the probabilities or times. They also mention that they are unsure if they are approaching the problem correctly.
  • #1
macca1994
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Homework Statement


Could someone please help me with this question?

A single-celled organism contains N particles, some of which are of type A, the others of
type B . The cell is said to be in state i , where 0<=i<=N if it contains exactly i particles
of type A. Daughter cells are formed by cell division, but fi rst each particle replicates itself;
the daughter cell inherits N particles chosen at random from the 2i particles of type A
and 2N-2i of type B in the parent cell.

Find the absorption probabilities and expected times to absorption for the case N = 3.


Homework Equations





The Attempt at a Solution


So far i have that the absorbing states are i=0 and i=3 but can't work out the probabilities or times to absorption. Don't know where to start now that i have my absorbing states
 
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  • #2
From state i, the (A,B) numbers of the daughter are determined by N independent "draws", with probabilities P(A) = i/N and P(B) = 1-P(A) in each draw.
 
  • #3
So is the probability of say reaching state 1 from state 2 1/5 from the number of possible combinations of the daughter cell or am i going about this the wrong way?
 
  • #4
macca1994 said:
So is the probability of say reaching state 1 from state 2 1/5 from the number of possible combinations of the daughter cell or am i going about this the wrong way?

I have said all I intend to say on the subject.
 

FAQ: Markov Chains and absorption probabilites

What are Markov Chains and absorption probabilities?

Markov Chains are mathematical models used to describe a sequence of events where the probability of transitioning from one state to another depends only on the current state. Absorption probabilities refer to the likelihood of ending up in a particular state after a certain number of transitions in a Markov Chain.

How are Markov Chains and absorption probabilities used in scientific research?

Markov Chains are used in various fields of science, such as biology, economics, and physics, to model and analyze complex systems and predict future outcomes. Absorption probabilities are particularly useful for understanding the long-term behavior of these systems.

What is the difference between transient and absorbing states in a Markov Chain?

Transient states in a Markov Chain are those that can be left and re-entered, while absorbing states are those that once entered, cannot be left. Transient states are typically the initial states, while absorbing states are the final states.

Can absorption probabilities be calculated for all Markov Chains?

No, absorption probabilities can only be calculated for irreducible Markov Chains, which are those that have a single communicating class. In other words, all states in the Markov Chain must be able to reach each other.

How can absorption probabilities be calculated for a Markov Chain?

Absorption probabilities can be calculated using various methods, such as the fundamental matrix method, the eigenvalue method, or the direct method. These methods involve manipulating the transition matrix of the Markov Chain and using linear algebra to solve for the absorption probabilities.

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