birth-death process in a two state system
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I'm trying to understand a stochastic process, and i'm not sure about a system that i'm studying.
Consider a two state system with a certain number $N$ of time-depending variables $x_i$ that can assume values 0,1.
At the initial time $t_0 = 0$ we have $$x_i(t_0) = 0 forall i=1,dots,N .$$ I assign to every variable a waiting time $t_i^{(lambda)}$, that is a random variate of an exponential distribution with parameter $lambda$; after its waiting time, the value of the variable $x_i$ switches from 0 to 1, so
$$ x_i(t_0 + t^{(lambda)}_i)=1 .$$
Now i assign a new waiting time $t^{(mu)}_i$, that is a random variate of a new exponential distribution with parameter $mu > lambda$, after which the value of $x_i$ switches back to the value 0. I let then the system evolve in time until the steady state is reached.
My question is: considering for example the arrivals from the state 0 to the state 1, this is NOT a Poisson process, right? In order to have it, the waiting time between arrivals should be exponentially distributed, and this is not the case. In other words, if i consider a Poisson process with parameter $lambda$ for the arrivals to the state 1, i should get a different process from the one that i have presented.
probability-theory stochastic-processes poisson-process birth-death-process
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$begingroup$
I'm trying to understand a stochastic process, and i'm not sure about a system that i'm studying.
Consider a two state system with a certain number $N$ of time-depending variables $x_i$ that can assume values 0,1.
At the initial time $t_0 = 0$ we have $$x_i(t_0) = 0 forall i=1,dots,N .$$ I assign to every variable a waiting time $t_i^{(lambda)}$, that is a random variate of an exponential distribution with parameter $lambda$; after its waiting time, the value of the variable $x_i$ switches from 0 to 1, so
$$ x_i(t_0 + t^{(lambda)}_i)=1 .$$
Now i assign a new waiting time $t^{(mu)}_i$, that is a random variate of a new exponential distribution with parameter $mu > lambda$, after which the value of $x_i$ switches back to the value 0. I let then the system evolve in time until the steady state is reached.
My question is: considering for example the arrivals from the state 0 to the state 1, this is NOT a Poisson process, right? In order to have it, the waiting time between arrivals should be exponentially distributed, and this is not the case. In other words, if i consider a Poisson process with parameter $lambda$ for the arrivals to the state 1, i should get a different process from the one that i have presented.
probability-theory stochastic-processes poisson-process birth-death-process
$endgroup$
add a comment |
$begingroup$
I'm trying to understand a stochastic process, and i'm not sure about a system that i'm studying.
Consider a two state system with a certain number $N$ of time-depending variables $x_i$ that can assume values 0,1.
At the initial time $t_0 = 0$ we have $$x_i(t_0) = 0 forall i=1,dots,N .$$ I assign to every variable a waiting time $t_i^{(lambda)}$, that is a random variate of an exponential distribution with parameter $lambda$; after its waiting time, the value of the variable $x_i$ switches from 0 to 1, so
$$ x_i(t_0 + t^{(lambda)}_i)=1 .$$
Now i assign a new waiting time $t^{(mu)}_i$, that is a random variate of a new exponential distribution with parameter $mu > lambda$, after which the value of $x_i$ switches back to the value 0. I let then the system evolve in time until the steady state is reached.
My question is: considering for example the arrivals from the state 0 to the state 1, this is NOT a Poisson process, right? In order to have it, the waiting time between arrivals should be exponentially distributed, and this is not the case. In other words, if i consider a Poisson process with parameter $lambda$ for the arrivals to the state 1, i should get a different process from the one that i have presented.
probability-theory stochastic-processes poisson-process birth-death-process
$endgroup$
I'm trying to understand a stochastic process, and i'm not sure about a system that i'm studying.
Consider a two state system with a certain number $N$ of time-depending variables $x_i$ that can assume values 0,1.
At the initial time $t_0 = 0$ we have $$x_i(t_0) = 0 forall i=1,dots,N .$$ I assign to every variable a waiting time $t_i^{(lambda)}$, that is a random variate of an exponential distribution with parameter $lambda$; after its waiting time, the value of the variable $x_i$ switches from 0 to 1, so
$$ x_i(t_0 + t^{(lambda)}_i)=1 .$$
Now i assign a new waiting time $t^{(mu)}_i$, that is a random variate of a new exponential distribution with parameter $mu > lambda$, after which the value of $x_i$ switches back to the value 0. I let then the system evolve in time until the steady state is reached.
My question is: considering for example the arrivals from the state 0 to the state 1, this is NOT a Poisson process, right? In order to have it, the waiting time between arrivals should be exponentially distributed, and this is not the case. In other words, if i consider a Poisson process with parameter $lambda$ for the arrivals to the state 1, i should get a different process from the one that i have presented.
probability-theory stochastic-processes poisson-process birth-death-process
probability-theory stochastic-processes poisson-process birth-death-process
edited Nov 26 '18 at 15:40
mminitr
asked Nov 26 '18 at 12:39
mminitrmminitr
62
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