Tutorials › Stochastic Processes › Rate Diagrams And Generator Entries

Birth-Death Processes · Tutorial 515 of 994

Rate Diagrams And Generator Entries

Build and check a birth-death generator from its rate diagram, and interpret its off-diagonal and diagonal entries.

Intermediate 10 min read

What You'll Learn

  • Translate each birth and death arrow into an off-diagonal generator entry.
  • Set diagonal entries to the negative total rate of leaving each state.
  • Handle absent transitions and zero-rate boundary arrows without adding invalid states.
  • Check row sums and recover rates from a generator matrix.
  • Apply the generator to a function on the state space and verify the result.

From a Rate Diagram to a Generator

A birth-death process moves among ordered states by single-step increases and decreases. A rate diagram records the rates attached to those possible moves; the generator collects the same information in a matrix, with one additional entry in each row that records the total rate of leaving that state. Constructing that diagonal entry correctly is essential: a generator is not a transition-probability matrix, and its rows sum to zero rather than one.

Let \((X_t)_{t\ge0}\) be a continuous-time Markov chain on a finite state space \(S\), defined on a probability space \((\Omega,\mathcal{F},\mathbb{P})\). Write \(P(t)=(p_{ij}(t))\), where \(p_{ij}(t)=\mathbb{P}(X_t=j\mid X_0=i)\). Its generator \(Q=(q_{ij})\) is given by the infinitesimal rates, when the limits exist:

$$ q_{ij}=\lim_{h\downarrow0}\frac{p_{ij}(h)}{h}\quad (i\ne j), \qquad q_{ii}=\lim_{h\downarrow0}\frac{p_{ii}(h)-1}{h}. $$

Thus an off-diagonal entry is the instantaneous rate of transition from \(i\) to \(j\). The diagonal entry is not a transition rate to a different state. For a finite-state chain, the rows of \(P(t)\) sum to one, and consequently each row of \(Q\) sums to zero. In particular, \(q_{ii}=-\sum_{j\ne i}q_{ij}\). The total exit rate from \(i\) is the nonnegative number \(-q_{ii}\).

Definition: On ordered states, a birth-death rate diagram assigns a birth rate \(\lambda_i\) to the arrow \(i\to i+1\) and a death rate \(\mu_i\) to the arrow \(i\to i-1\), wherever those states and arrows are allowed. All other moves between distinct states have rate zero. The corresponding generator has \(q_{i,i+1}=\lambda_i\), \(q_{i,i-1}=\mu_i\), \(q_{ii}=-(\lambda_i+\mu_i)\), and zero entries for other destinations. At a boundary, an absent arrow contributes zero: equivalently, its rate is set to zero in this formula.

The phrase “wherever allowed” matters. On \(S=\{0,1,\ldots,N\}\), there is no state \(-1\) or \(N+1\), so the rates for arrows out of the state space are set to zero: \(\mu_0=0\) and \(\lambda_N=0\). A boundary may also be absorbing or have some other allowed behavior, depending on its rates. The diagram specifies which transitions are possible; the generator records their rates and the negative sum of the rates out of each state.

Theorem (Rate-diagram construction). Suppose a finite-state continuous-time Markov chain has only the birth and death transitions shown in a rate diagram. At each state \(i\), let the rates of the allowed arrows be \(\lambda_i\) and \(\mu_i\), setting a rate to zero when its corresponding arrow is absent. Then its generator entries are \(q_{i,i+1}=\lambda_i\), \(q_{i,i-1}=\mu_i\), \(q_{ii}=-(\lambda_i+\mu_i)\), and \(q_{ij}=0\) for all other \(j\). Conversely, these entries give exactly the specified rates and no other transitions, and every row sums to zero.

Proof. Fix a state \(i\). By the definition of an off-diagonal generator entry, the rate on the arrow \(i\to i+1\) is \(q_{i,i+1}=\lambda_i\), and the rate on \(i\to i-1\) is \(q_{i,i-1}=\mu_i\). Every other move to a distinct state is excluded by the rate diagram, so its rate, and therefore its off-diagonal generator entry, is zero. The row-sum condition for a finite-state generator gives \(q_{ii}=-\sum_{j\ne i}q_{ij}=-(\lambda_i+\mu_i)\), where absent arrows have rate zero. This proves the stated form. Conversely, in the displayed form the two possible off-diagonal entries have precisely the specified rates, all other off-diagonal entries vanish, and the row sum is \(\lambda_i+\mu_i-(\lambda_i+\mu_i)=0\). Since \(i\) was arbitrary, the claims hold in every row. \(\square\)

Reading the Entries and Checking the Matrix

There are two routine checks when converting a diagram. First, each off-diagonal entry must correspond to an arrow with the same rate; do not place a rate on the diagonal. Second, the diagonal entry must be the negative sum of all off-diagonal entries in that row. It is therefore zero at an absorbing state, negative at any state with a positive exit rate, and never positive.

Worked Example: A Four-State Chain with Interior Births and Deaths

Take \(S=\{0,1,2,3\}\). The birth rates from states \(0,1,2\) are \(\lambda_0=2\), \(\lambda_1=3\), and \(\lambda_2=\tfrac12\); set \(\lambda_3=0\), since state \(4\) is outside \(S\). The death rates are \(\mu_0=0\), \(\mu_1=1\), \(\mu_2=2\), and \(\mu_3=3\). Before constructing \(Q\), the total rates out of states \(0,1,2,3\) are respectively \(2\), \(3+1=4\), \(\tfrac12+2=\tfrac52\), and \(0+3=3\).

Using the state order \(0,1,2,3\), place each birth and death rate in its corresponding off-diagonal entry and negate the total rate on the diagonal:

$$ Q= \begin{bmatrix} -2&2&0&0\\ 1&-4&3&0\\ 0&2&-\tfrac52&\tfrac12\\ 0&0&3&-3 \end{bmatrix}. $$

Check: the row sums are \(-2+2=0\), \(1-4+3=0\), \(2-\tfrac52+\tfrac12=0\), and \(3-3=0\). The off-diagonal entries reproduce the given rates: for example, from state \(2\) the rates to \(1\) and \(3\) are \(2\) and \(\tfrac12\), and the total exit rate is \(-q_{22}=\tfrac52\). At state \(3\), the only arrow is to \(2\), with rate \(3\); the zero entry for a birth from \(3\) does not create an extra state.

For a countably infinite state space, the same entry rule defines a conservative \(Q\)-matrix when the off-diagonal rates in each row have finite sum: the diagonal is the negative of that sum, so each row sums to zero. This algebraic row-sum property alone does not guarantee that the matrix generates a conservative continuous-time Markov chain. Nonexplosion is also required. The examples here are finite-state, where this countable-state qualification does not arise.

Worked Example: An Absorbing Boundary and a Reflecting Upper Boundary

Let \(S=\{0,1,2,3\}\). State \(0\) is absorbing, so set \(\lambda_0=\mu_0=0\). From state \(1\), the rates to \(0\) and \(2\) are \(\mu_1=1\) and \(\lambda_1=2\). From state \(2\), the rates to \(1\) and \(3\) are \(\mu_2=3\) and \(\lambda_2=\tfrac12\). At state \(3\), set \(\lambda_3=0\) and \(\mu_3=4\); the upper boundary has a downward arrow but no upward arrow. The total rates out are \(0\), \(1+2=3\), \(3+\tfrac12=\tfrac72\), and \(4+0=4\).

In the state order \(0,1,2,3\), the generator is

$$ Q= \begin{bmatrix} 0&0&0&0\\ 1&-3&2&0\\ 0&3&-\tfrac72&\tfrac12\\ 0&0&4&-4 \end{bmatrix}. $$

Check: its row sums are \(0\), \(1-3+2=0\), \(3-\tfrac72+\tfrac12=0\), and \(4-4=0\). Row \(0\) is all zero, as required for an absorbing state. In row \(3\), \(q_{32}=4\) gives the downward rate and \(q_{33}=-4\) records the negative total exit rate. The zero in the birth position means that no transition to state \(4\) is included.

What the Generator Does to a Function

Besides displaying individual rates, \(Q\) describes the instantaneous change in the expected value of a function of the state. For a real-valued function \(f\) on a finite state space, the generator acts by matrix multiplication:

$$ (Qf)(i)=\sum_{j\in S}q_{ij}f(j) =\lambda_i\bigl(f(i+1)-f(i)\bigr) +\mu_i\bigl(f(i-1)-f(i)\bigr). $$

In this expression, a term for an absent arrow is zero. The differences show why the diagonal is negative: it subtracts the current value \(f(i)\) once for each possible outgoing rate. Equivalently, the finite-state generator definition gives \(\lim_{h\downarrow0}(\mathbb{E}_i[f(X_h)]-f(i))/h=(Qf)(i)\).

Proposition (Generator action on functions): For a finite-state continuous-time Markov chain with generator \(Q\), and any function \(f:S\to\mathbb{R}\), the instantaneous rate of change of its expected value from initial state \(i\) is \((Qf)(i)=\sum_jq_{ij}f(j)\). For a birth-death chain this equals the sum of the birth and death rates multiplied by the corresponding changes in \(f\).

Proof. Since \(S\) is finite, write \(\mathbb{E}_i[f(X_h)]=\sum_{j\in S}p_{ij}(h)f(j)\). Subtract \(f(i)\), and use \(\sum_jp_{ij}(h)=1\), to obtain \[ \mathbb{E}_i[f(X_h)]-f(i) =\sum_{j\in S}\bigl(p_{ij}(h)-\mathbf{1}_{\{i=j\}}\bigr)f(j). \] Divide by \(h>0\) and take \(h\downarrow0\). The sum has finitely many terms, so the limit can be taken term by term; each coefficient converges to \(q_{ij}\) by the definition of the generator. The limit is \(\sum_jq_{ij}f(j)=(Qf)(i)\). For a birth-death chain, substitute \(q_{i,i+1}=\lambda_i\), \(q_{i,i-1}=\mu_i\), and \(q_{ii}=-(\lambda_i+\mu_i)\). The result is \(\lambda_i(f(i+1)-f(i))+\mu_i(f(i-1)-f(i))\), with absent-arrow terms zero. \(\square\)

Worked Example: Applying the Generator to a State Function

On \(S=\{0,1,2\}\), take \(\lambda_0=\tfrac32\), \(\lambda_1=\tfrac12\), \(\lambda_2=0\), and \(\mu_0=0\), \(\mu_1=2\), \(\mu_2=\tfrac14\). The total rates out are \(\tfrac32\), \(\tfrac12+2=\tfrac52\), and \(\tfrac14\). Hence

$$ Q= \begin{bmatrix} -\tfrac32&\tfrac32&0\\ 2&-\tfrac52&\tfrac12\\ 0&\tfrac14&-\tfrac14 \end{bmatrix}. $$

Check before using: the row sums are \(-\tfrac32+\tfrac32=0\), \(2-\tfrac52+\tfrac12=0\), and \(\tfrac14-\tfrac14=0\). Let \(f(0)=4\), \(f(1)=1\), and \(f(2)=5\), so \(f\) is the column vector \((4,1,5)^\mathsf{T}\). Direct multiplication gives

$$ Qf= \begin{bmatrix} -\tfrac32(4)+\tfrac32(1)\\ 2(4)-\tfrac52(1)+\tfrac12(5)\\ \tfrac14(1)-\tfrac14(5) \end{bmatrix} = \begin{bmatrix} -\tfrac92\\ 8\\ -1 \end{bmatrix}. $$

The rate-difference formula gives the same values. At state \(0\), \(\tfrac32(f(1)-f(0))=\tfrac32(1-4)=-\tfrac92\). At state \(1\), \(2(f(0)-f(1))+\tfrac12(f(2)-f(1))=2(4-1)+\tfrac12(5-1)=6+2=8\). At state \(2\), \(\tfrac14(f(1)-f(2))=\tfrac14(1-5)=-1\). Thus the formula and matrix multiplication agree in every state.

A Common Error: Putting the Exit Rate in the Wrong Place

A frequent mistake is to put a positive total exit rate on the diagonal, or to use a zero diagonal because there is no arrow from a state to itself. Neither convention gives the generator. For example, in the first example, state \(1\) has a death rate \(1\) and a birth rate \(3\). A wrong row would be \((1,4,3,0)\), using a positive diagonal entry \(4\). Its row sum is \(1+4+3=8\), not zero, and its diagonal cannot represent a transition rate to a distinct state. The correct row is \((1,-4,3,0)\), whose sum is \(1-4+3=0\), and whose diagonal is the negative total exit rate.

Another incorrect calculation would set \(q_{11}=-\lambda_1=-3\), forgetting the death arrow. The resulting row \((1,-3,3,0)\) sums to \(1\), so it fails the generator check. The correction is to include every outgoing arrow before negating: \(-(\mu_1+\lambda_1)=-(1+3)=-4\). In general, read the off-diagonal entries first, sum them, and only then write the diagonal as the negative of that sum.

Once a generator has been built, its entries can be checked in both directions: each allowed arrow appears exactly once as an off-diagonal rate, and the diagonal balances all outgoing rates. The total exit rate is \(-q_{ii}\), not \(q_{ii}\); it is a nonnegative rate, whereas the diagonal generator entry is nonpositive.

Check Your Understanding

Use the rate-diagram rule and verify each row sum in your calculations.

  1. On \(S=\{0,1,2\}\), let \(\lambda_0=1\), \(\lambda_1=2\), \(\lambda_2=0\), \(\mu_0=0\), \(\mu_1=3\), and \(\mu_2=4\). Write the generator in state order \(0,1,2\).
  2. For the chain in Question 1, what is the total exit rate from state \(1\), and which generator entry records its negative?
  3. If a boundary state \(N\) has no allowed birth arrow, what value should be used for \(\lambda_N\) when forming its diagonal entry?
  4. For a birth-death chain and a function \(f\), write \((Qf)(i)\) in terms of \(\lambda_i,\mu_i\), and the neighboring values of \(f\).
  5. Explain why a matrix with nonnegative off-diagonal rates but a row sum different from zero cannot be the generator of a finite-state continuous-time Markov chain.