Math 2.0

Probability theory and stochastic processes Definition

Doob's h-transform

Builds on Markov kernelExcessive functionWiener processInfinitesimal generator of a Markov process

Definition

Let (E,ℰ)(E, \mathcal{E}) be a measurable space and let (Pt)t≥0(P_t)_{t \ge 0} be a sub-Markov transition semigroup on EE. So the PtP_t are kernels as for Markov kernels but with Pt(x,E)≤1P_t(x, E) \le 1, and PsPt=Ps+tP_s P_t = P_{s+t}. Let h ⁣:E→(0,∞)h \colon E \to (0, \infty) be a measurable function that is excessive for (Pt)(P_t). The h-transform of (Pt)(P_t) is the family of kernels Pth(x,dy)=h(y)h(x) Pt(x,dy),t≥0, x∈E.P^h_t(x, dy) = \frac{h(y)}{h(x)} \, P_t(x, dy), \qquad t \ge 0, \ x \in E. A Markov process with transition semigroup (Pth)(P^h_t) is called an h-process.

Remarks

The kernels PthP^h_t form a sub-Markov semigroup. Indeed, Pth(x,E)=Pth(x)/h(x)≤1P^h_t(x, E) = P_t h(x)/h(x) \le 1 since hh is excessive, and ∫Psh(x,dy)Pth(y,dz)=h(z)h(x)∫Ps(x,dy)Pt(y,dz)=h(z)h(x)Ps+t(x,dz).\int P^h_s(x, dy) P^h_t(y, dz) = \frac{h(z)}{h(x)} \int P_s(x, dy) P_t(y, dz) = \frac{h(z)}{h(x)} P_{s+t}(x, dz). If Pth=hP_t h = h for all tt, then each PthP^h_t is a Markov kernel and the h-process has infinite lifetime. Example: let (Pt)(P_t) be Brownian motion on (0,∞)(0, \infty) killed at 00, and h(x)=xh(x) = x. Then Pth(x)=Ex[Bt;t<τ0]=xP_t h(x) = E^x[B_t ; t < \tau_0] = x by optional stopping, so hh is invariant, and the h-process is the three-dimensional Bessel process, Brownian motion conditioned never to hit 00. Non-example: for Brownian motion on ℝ\R and h(x)=1+x2h(x) = 1 + x^2, one has Pth(x)=1+x2+t>h(x)P_t h(x) = 1 + x^2 + t > h(x), so hh is not excessive and Pth(x,ℝ)>1P^h_t(x, \R) > 1. If LL is the infinitesimal generator of (Pt)(P_t), the generator of the h-process acts by Lhf=h−1L(hf)L^h f = h^{-1} L(h f) on suitable ff. For L=12ΔL = \frac12 \Delta and harmonic hh, this is Lhf=12Δf+∇(log⁡h)⋅∇fL^h f = \frac12 \Delta f + \nabla(\log h) \cdot \nabla f, a Brownian motion with drift ∇log⁡h\nabla \log h.

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  1. Excessive function concept
  2. Infinitesimal generator of a Markov process concept
  3. Markov kernel concept
  4. Wiener process concept
  5. Doob's h-transform definition

Further reading

  1. Web https://en.wikipedia.org/wiki/Doob%27s_h-transform
About this page
TagDK3492C42N
URIm2://DK3492C42N
Kinddefinition
WritingW0 stub
MSC60J45, 60J25
DefinesDoob's h-transform
UsesMarkov kernel, Excessive function, Wiener process, Infinitesimal generator of a Markov process

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