Markov Birth Death Chain Show Stationary Distribution

In summary: BhxSGaIn summary, the logistics model is a birth and death chain with immigration at a constant rate, birth at a linearly increasing rate, and death at a rate that also increases linearly due to crowding. The system has a stationary distribution, which can be shown by using the birth and death process equation and the fact that a, B, S, and y are all positive. By showing that the product of the birth and death rates is a finite value, it can be concluded that the sum of all possible probabilities is also finite.
  • #1
crazy_craig
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Homework Statement



Logistics model: Consider the birth and death chain with birth rates π(n)=a + Bn and death rates μ(n) = (S + yn)n where all four constants (a, B, S, y) are positive. In words, there is immigration at rate a, each particle gives birth at rate B and dies at rate S+yn, i.e., at a rate which starts at rate S and increases linearly due to crowding. Show that the system has a stationary distribution.


Homework Equations



Since this is a birth and death process, we know;

π(n) = λ(n-1)*λ(n-2)* ...*λ(0) * π(0) / μ(n) * μ(n-1) * ... * μ(1)

where the rate from n to n+1 = λ(n) and the rate from n to n-1= μ(n)

The Attempt at a Solution



Using this, we have:

π(n) = π(0) * ∏i=0n-1 λ(i)/μ(i+1)

π(n) = π(0) * ∏i=0n-1 [ a + Bi] / [ (S+y(i + 1)) * (i+1) ]

And this is where I'm stuck. If I can show that the Ʃn π(n) < ∞ , then I could pick π(0) to make the sum 1. I guess I need to show that the above product is a finite value or at least less than some finite value, and then Ʃn π(n) would also be finite...
 
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  • #2
crazy_craig said:

Homework Statement



Logistics model: Consider the birth and death chain with birth rates π(n)=a + Bn and death rates μ(n) = (S + yn)n where all four constants (a, B, S, y) are positive. In words, there is immigration at rate a, each particle gives birth at rate B and dies at rate S+yn, i.e., at a rate which starts at rate S and increases linearly due to crowding. Show that the system has a stationary distribution.


Homework Equations



Since this is a birth and death process, we know;

π(n) = λ(n-1)*λ(n-2)* ...*λ(0) * π(0) / μ(n) * μ(n-1) * ... * μ(1)

where the rate from n to n+1 = λ(n) and the rate from n to n-1= μ(n)

The Attempt at a Solution



Using this, we have:

π(n) = π(0) * ∏i=0n-1 λ(i)/μ(i+1)

π(n) = π(0) * ∏i=0n-1 [ a + Bi] / [ (S+y(i + 1)) * (i+1) ]

And this is where I'm stuck. If I can show that the Ʃn π(n) < ∞ , then I could pick π(0) to make the sum 1. I guess I need to show that the above product is a finite value or at least less than some finite value, and then Ʃn π(n) would also be finite...

Since a, B, S and y are > 0 we have [tex] 0 < f(i) \equiv \frac{a + Bi}{S + y(i+1)} \leq K [/tex] for some easily-computable constant K = K(a,B,S,y). Thus
[tex]0 < \prod_{i=0}^{n-1} \frac{f(i)}{i+1} \leq \frac{K^n}{n!}.[/tex]

RGV
 

Related to Markov Birth Death Chain Show Stationary Distribution

1. What is a Markov Birth Death Chain?

A Markov Birth Death Chain is a type of stochastic process in which the state of a system changes over time according to a set of probabilities. It is used to model systems that have a finite number of states and experience births and deaths.

2. What is the purpose of a Markov Birth Death Chain?

The purpose of a Markov Birth Death Chain is to analyze the behavior and evolution of a system over time. By studying the transition probabilities between states, we can determine the long-term behavior of the system and its steady-state distribution.

3. What is a stationary distribution in a Markov Birth Death Chain?

A stationary distribution in a Markov Birth Death Chain is a set of probabilities that represent the long-term behavior of the system. It is a state distribution that remains unchanged over time, regardless of the initial state of the system.

4. How is the stationary distribution calculated in a Markov Birth Death Chain?

The stationary distribution is calculated by solving a set of linear equations known as the Chapman-Kolmogorov equations. These equations describe the transition probabilities between states and can be used to find the steady-state distribution.

5. What factors can affect the stationary distribution in a Markov Birth Death Chain?

The stationary distribution in a Markov Birth Death Chain can be affected by the birth and death rates of the system, as well as the transition probabilities between states. Changes in these parameters can alter the long-term behavior of the system and its steady-state distribution.

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