Paper 4, Section II, 27 K27 \mathrm{~K}

Applied Probability | Part II, 2021

Let (X(t))t⩾0(X(t))_{t \geqslant 0} be a continuous-time Markov process with state space I={1,…,n}I=\{1, \ldots, n\} and generator Q=(qij)i,j∈IQ=\left(q_{i j}\right)_{i, j \in I} satisfying qij=qjiq_{i j}=q_{j i} for all i,j∈Ii, j \in I. The local time up to time t>0t>0 of XX is the random vector L(t)=(Li(t))i∈I∈RnL(t)=\left(L_{i}(t)\right)_{i \in I} \in \mathbb{R}^{n} defined by

Li(t)=∫0t1X(s)=ids(i∈I)L_{i}(t)=\int_{0}^{t} 1_{X(s)=i} d s \quad(i \in I)

(a) Let f:I×Rn→Rf: I \times \mathbb{R}^{n} \rightarrow \mathbb{R} be any function that is differentiable with respect to its second argument, and set

ft(i,ℓ)=Eif(X(t),ℓ+L(t)),(i∈I,ℓ∈Rn)f_{t}(i, \ell)=\mathbb{E}_{i} f(X(t), \ell+L(t)), \quad\left(i \in I, \ell \in \mathbb{R}^{n}\right)

Show that

∂∂tft(i,ℓ)=Mft(i,ℓ)\frac{\partial}{\partial t} f_{t}(i, \ell)=M f_{t}(i, \ell)

where

Mf(i,ℓ)=∑j∈Iqijf(j,ℓ)+∂∂ℓif(i,ℓ)M f(i, \ell)=\sum_{j \in I} q_{i j} f(j, \ell)+\frac{\partial}{\partial \ell_{i}} f(i, \ell)

(b) For y∈Rny \in \mathbb{R}^{n}, write y2=(yi2)i∈I∈[0,∞)ny^{2}=\left(y_{i}^{2}\right)_{i \in I} \in[0, \infty)^{n} for the vector of squares of the components of yy. Let f:I×Rn→Rf: I \times \mathbb{R}^{n} \rightarrow \mathbb{R} be a function such that f(i,ℓ)=0f(i, \ell)=0 whenever ∑j∣ℓj∣⩾T\sum_{j}\left|\ell_{j}\right| \geqslant T for some fixed TT. Using integration by parts, or otherwise, show that for all ii

−∫Rnexp⁡(12yTQy)yi∑j=1nyjMf(j,12y2)dy=∫Rnexp⁡(12yTQy)f(i,12y2)dy,-\int_{\mathbb{R}^{n}} \exp \left(\frac{1}{2} y^{T} Q y\right) y_{i} \sum_{j=1}^{n} y_{j} M f\left(j, \frac{1}{2} y^{2}\right) d y=\int_{\mathbb{R}^{n}} \exp \left(\frac{1}{2} y^{T} Q y\right) f\left(i, \frac{1}{2} y^{2}\right) d y,

where yTQyy^{T} Q y denotes ∑k,m∈Iykqkmym\sum_{k, m \in I} y_{k} q_{k m} y_{m}.

(c) Let g:Rn→Rg: \mathbb{R}^{n} \rightarrow \mathbb{R} be a function with g(ℓ)=0g(\ell)=0 whenever ∑j∣ℓj∣⩾T\sum_{j}\left|\ell_{j}\right| \geqslant T for some fixed TT. Given t>0,j∈It>0, j \in I, now let

f(i,ℓ)=Ei[g(ℓ+L(t))1X(t)=j]f(i, \ell)=\mathbb{E}_{i}\left[g(\ell+L(t)) 1_{X(t)=j}\right]

in part (b) and deduce, using part (a), that

∫Rnexp⁡(12yTQy)yiyjg(12y2)dy=∫Rnexp⁡(12yTQy)(∫0∞Ei[1X(t)=jg(12y2+L(t))]dt)dy\begin{aligned} \int_{\mathbb{R}^{n}} \exp \left(\frac{1}{2} y^{T} Q y\right) y_{i} y_{j} g\left(\frac{1}{2} y^{2}\right) d y \\ &=\int_{\mathbb{R}^{n}} \exp \left(\frac{1}{2} y^{T} Q y\right)\left(\int_{0}^{\infty} \mathbb{E}_{i}\left[1_{X(t)=j} g\left(\frac{1}{2} y^{2}+L(t)\right)\right] d t\right) d y \end{aligned}

[You may exchange the order of integrals and derivatives without justification.]

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