Long-run proportion in state ′A′

In summary: So for the device, Exp(3) means an exponential random variable with a rate of 3 per hour. In summary, the device can be in three states: A, B, and C, and operates with an exponentially distributed amount of time in state A with a mean of 12 minutes. After that, with a probability of 0.6, it goes to state B, and with a probability of 0.4, it goes to state C. In state B, it moves to state C after an exponential amount of time with a rate of 3 per hour. In state C, it transitions to state A at a rate of 1 per hour and to state B at a rate of 2 per hour.
  • #1
iikii
5
0
Moved from a technical forum, so homework template missing
The question asks:

A physical device can be in three states: A,B,C. The device operates as follows (all time units are in hours):

The device spends an exponentially distributed amount of time in stateAA (with mean of 12minutes) and then with probability 0.6 goes to state B, and with prob.0.4goes to state C. When in state B, the device moves to state C after an Exp(3) amount of time. When in state C, the device goes to state A at rate 1/hour, and to state B at rate 2/hour. Let Xt represent the device state at time tt, and suppose X0=′A0′. What is the long-run proportion of time the device spends in state ′A′.

So I am thinking about maybe I should calculate the probability the device is in state A after 30 minutes and the probability the device is in state A after 30 minutes given that it was in state B after 5 minutes and in state C after 10 minutes. But what not sure how to do. Any help is appreciated!
 
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  • #2
iikii said:
The question asks:

A physical device can be in three states: A,B,C. The device operates as follows (all time units are in hours):

The device spends an exponentially distributed amount of time in stateAA (with mean of 12minutes) and then with probability 0.6 goes to state B, and with prob.0.4goes to state C. When in state B, the device moves to state C after an Exp(3) amount of time. When in state C, the device goes to state A at rate 1/hour, and to state B at rate 2/hour. Let Xt represent the device state at time tt, and suppose X0=′A0′. What is the long-run proportion of time the device spends in state ′A′.

So I am thinking about maybe I should calculate the probability the device is in state A after 30 minutes and the probability the device is in state A after 30 minutes given that it was in state B after 5 minutes and in state C after 10 minutes. But what not sure how to do. Any help is appreciated!

Does Exp(3) mean an exponential random variable with a rate of 3 (per hour), or does it mean an exponential with a mean of 3 (hours)?

After obtaining the transition-rate matrix, this problem is a simple example of using the standard equations to find the long-run limiting state probabilities. Finding state probabilities for finite times (such as t = 30 min = 0.5 hr) would require finding transient behavior, and finding that would require solving the 3x3 system of coupled linear differential equations. It is much, much easier to determine the limiting values. This material is covered in just about every textbook on the subject.
 
  • #3
Ray Vickson said:
Does Exp(3) mean an exponential random variable with a rate of 3 (per hour), or does it mean an exponential with a mean of 3 (hours)?

After obtaining the transition-rate matrix, this problem is a simple example of using the standard equations to find the long-run limiting state probabilities. Finding state probabilities for finite times (such as t = 30 min = 0.5 hr) would require finding transient behavior, and finding that would require solving the 3x3 system of coupled linear differential equations. It is much, much easier to determine the limiting values. This material is covered in just about every textbook on the subject.
Thanks! So I got the probability going from A to B P(A,B)=0.6, P(A,C)=0.4, P(B,C)=1, but what are the probabilities of P(C,B) and P(C,A)?
 
  • #4
iikii said:
Thanks! So I got the probability going from A to B P(A,B)=0.6, P(A,C)=0.4, P(B,C)=1, but what are the probabilities of P(C,B) and P(C,A)?

So, what is your answer to my question about the meaning of Exp(3)?

You need to construct a continuous-time Markov chain transition rate matrix.
 
  • #5
Ray Vickson said:
So, what is your answer to my question about the meaning of Exp(3)?

You need to construct a continuous-time Markov chain transition rate matrix.
I think it is the rate parameter.
 

Related to Long-run proportion in state ′A′

What is a long-run proportion in state ′A′?

A long-run proportion in state ′A′ refers to the percentage of observations or data points that fall into a particular state ′A′ over a long period of time. It is used to analyze the relative frequency or occurrence of a certain event or phenomenon.

How is a long-run proportion in state ′A′ calculated?

To calculate a long-run proportion in state ′A′, you need to divide the number of observations in state ′A′ by the total number of observations. This will give you a decimal value, which can then be converted to a percentage by multiplying it by 100.

Why is a long-run proportion in state ′A′ important?

A long-run proportion in state ′A′ is important because it can provide insights into the likelihood of a certain event or outcome occurring over a long period of time. It can also help identify patterns or trends in the data and inform decision-making.

What factors can affect the long-run proportion in state ′A′?

The long-run proportion in state ′A′ can be affected by various factors such as changes in the environment, different data collection methods, or human behavior. It is important to consider these factors when interpreting the results.

How can a long-run proportion in state ′A′ be used in scientific research?

A long-run proportion in state ′A′ can be used in scientific research to analyze and compare the frequency of different events or phenomena. It can also be used to test hypotheses and make predictions about future outcomes based on past data.

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