Classification of states of continuous-time chains

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Let qi,i+1=2i−1q_{i,i+1} = 2^{i-1} for all i≥0i \geq 0 and let qi,i−1=2i−1q_{i,i-1} = 2^{i-1} for all i≥1i \geq 1. All other transition rates are 00.

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(a)

Solve the steady-state equations and show that pi=2−i−1p_{i} = 2^{-i-1} for all i≥0i \geq 0.

(b)

Find the transition probabilities for the embedded Markov chain and show that the chain is null recurrent.

(c)

For any state ii, consider the renewal process for which the Markov process starts in state ii and renewals occur on each transition to state ii. Show that, for each i≥1i \geq 1, the expected inter-renewal interval is equal to 22.

(d)

Show that the expected number of transitions between each entry into state ii is infinite. Explain why this does not mean that an infinite number of transitions can occur in a finite time.

Remark.
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Hint for (c): Use renewal-reward theory.

Let X=(X(t):t≥0)X = (X(t): t \geq 0) be a Markov chain on Z\mathbb {Z} with generator G=(gij)\mathbf{G} = \left(g_{i j}\right) given by

gi,i−1=i2+1,gi,i=−2(i2+1),gi,i+1=i2+1,i∈Z g_{i, i-1} = i^{2}+1, \quad g_{i, i} = -2\left(i^{2}+1\right), \quad g_{i, i+1} = i^{2}+1, \quad i \in \mathbb {Z}

Show that XX is recurrent. Is XX positive recurrent?

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Let XX be a Markov chain on Z\mathbb {Z} with generator G=(gij)G = \left(g_{i j}\right) satisfying

gi,i−1=3∣i∣,gi,i=−3∣i∣+1,gi,i+1=2⋅3∣i∣,i∈Z g_{i, i-1} = 3^{\left|i\right|}, \quad g_{i, i} = -3^{\left|i\right|+1}, \quad g_{i, i+1} = 2 \cdot 3^{\left|i\right|}, \quad i \in \mathbb {Z}

Show that XX is transient, but has an invariant distribution. Explain.

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Let XX be a continuous-time Markov chain with generator G=(gij)\mathbf{G} = \left(g_{i j}\right). Show that XX is irreducible if and only if for any pair i,ji, j of distinct states there exists a sequence i,k1,k2,…,kn,ji, k_{1}, k_{2}, \ldots , k_{n}, j of distinct states such that gi,k1gk1,k2⋯gkn,j>0g_{i, k_{1}} g_{k_{1}, k_{2}} \cdots g_{k_{n}, j} > 0.

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Let XX be a continuous-time Markov chain with transition probabilities pij(t)p_{i j}(t) and define Fi=inf⁡{t>T1:X(t)=i}F_{i} = \inf \left\{ t > T_{1}: X(t) = i\right\} where T1T_{1} is the time of the first jump of XX. Show that, if gii≠0g_{i i} \neq 0, then Pi(Fi<∞)=1\mathbb {P}_{i}\left(F_{i} < \infty \right) = 1 if and only if ii is recurrent.

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Let XX be the simple symmetric random walk on the integers in continuous time, so that

pi,i+1(h)=pi,i−1(h)=12λh+o(h) p_{i, i+1}(h) = p_{i, i-1}(h) = \frac{1}{2} \lambda h+\mathrm{o}(h)

Show that the walk is recurrent. Let TT be the time spent visiting mm during an excursion from 0. Find the distribution of TT.

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Let ii be a transient state of a continuous-time Markov chain XX with X(0)=iX(0) = i. Show that the total time spent in state ii has an exponential distribution.

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Let XX be an asymmetric simple random walk in continuous time on the non-negative integers with retention at 00, so that

pij(h)={λh+o(h) if j=i+1,i≥0μh+o(h) if j=i−1,i≥1 p_{i j}(h) = \begin{cases} \lambda h+\mathrm{o}(h) & \text{ if } j = i+1, i \geq 0 \\ \mu h+\mathrm{o}(h) & \text{ if } j = i-1, i \geq 1\end{cases}

Suppose that X(0)=0X(0) = 0 and λ>μ\lambda > \mu. Show that the total time VrV_{r} spent in state rr is exponentially distributed with parameter λ−μ\lambda -\mu.

Assume now that X(0)X(0) has some general distribution with probability generating function GG. Find the expected amount of time spent at 0 in terms of GG.

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Let Zn=X(nh)Z_{n} = X(n h) where XX is a Markov chain and h>0h > 0. Show that ii is recurrent for ZZ if and only if it is recurrent for XX. Show that ZZ is irreducible if and only if XX is irreducible.

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Prove that, for the embedded Markov chain ξn∗\xi_{n}^{*}, the states xix_{i} and xjx_{j} communicate if and only if pij(t)>0p_{ij}(t) > 0 for all t>0t > 0.

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Let a jump Markov process with values 1, 2, 3, 4, and 5 be defined by the transition intensity matrix

Q=(−410032−631000−550004−4000000). Q = \begin{pmatrix} -4 & 1 & 0 & 0 & 3 \\ 2 & -6 & 3 & 1 & 0 \\ 0 & 0 & -5 & 5 & 0 \\ 0 & 0 & 4 & -4 & 0 \\ 0 & 0 & 0 & 0 & 0 \end{pmatrix}.

Find the transition probability matrix and classify the states.

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A homogeneous jump Markov process with values 1, 2, 3, 4, 5, and 6 is given by the transition intensity matrix

Q=(−5111110−4130020−42000000000000−220003−30). Q = \begin{pmatrix} -5 & 1 & 1 & 1 & 1 & 1 \\ 0 & -4 & 1 & 3 & 0 & 0 \\ 2 & 0 & -4 & 2 & 0 & 0 \\ 0 & 0 & 0 & 0 & 0 & 0 \\ 0 & 0 & 0 & 0 & -2 & 2 \\ 0 & 0 & 0 & 3 & -3 & 0 \end{pmatrix}.
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(a)

Find the transition probability matrix of the embedded Markov chain and use it to classify the states and find the absorption probabilities for each of the essential state classes.

(b)

Find all stationary distributions for the Markov process and for the embedded Markov chain. Do they coincide? Why?

A homogeneous jump Markov process ξ(t)\xi (t) with values 1, 2, 3, 4, 5, and 6 is given by the transition intensity matrix

Q=(−2101001−4110100−4040000−2020040−4000200−2). Q = \begin{pmatrix} -2 & 1 & 0 & 1 & 0 & 0 \\ 1 & -4 & 1 & 1 & 0 & 1 \\ 0 & 0 & -4 & 0 & 4 & 0 \\ 0 & 0 & 0 & -2 & 0 & 2 \\ 0 & 0 & 4 & 0 & -4 & 0 \\ 0 & 0 & 2 & 0 & 0 & -2 \end{pmatrix}.
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(a)

Find the transition probability matrix of the embedded Markov chain ξn∗\xi_{n}^{*}. Use it to classify the states and find the absorption probabilities for each of the essential classes.

(b)

Find all stationary distributions of the process ξ(t)\xi (t) and of the embedded Markov chain ξn∗\xi_{n}^{*}. Do they coincide? Why?

(c)

Find the limiting distributions for the Markov process and for the Markov chain, if ξ(0)=2\xi (0) = 2.

(d)

Find the limiting distributions for the Markov process and for the Markov chain, if ξ(0)=3\xi (0) = 3.

References

[Gal13] Gallager R.G. Stochastic Processes: Theory for Applications. Cambridge University Press, 2013.
[GrSt20] Grimmett G.R., Stirzaker D.R. Probability and Random Processes. 4th ed., Oxford University Press, 2020.
[КоФо03] Коршунов Д.А., Фосс С.Г. Сборник задач и упражнений по теории вероятностей. 2-е изд., Новосибирский государственный университет, 2003.