Analysis for Diffusion Processes on Riemannian Manifolds : by Feng-Yu Wang

By Feng-Yu Wang

Stochastic research on Riemannian manifolds with no boundary has been good demonstrated. even if, the research for reflecting diffusion methods and sub-elliptic diffusion approaches is way from whole. This e-book includes fresh advances during this course besides new rules and effective arguments, that are the most important for extra advancements. Many effects contained the following (for instance, the formulation of the curvature utilizing derivatives of the semigroup) are new between latest monographs even within the case with out boundary.

Readership: Graduate scholars, researchers and execs in chance thought, differential geometry and partial differential equations.

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Extra resources for Analysis for Diffusion Processes on Riemannian Manifolds : Advanced Series on Statistical Science and Applied Probability

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1) Let f ∈ Bb (E) be positive. 1) to 1 + εf in place of f for ε > 0, we have Φ(1 + εP f (x)) ≤ {P Φ(1 + εf )(y)}eΨ(x,y) , x, y ∈ E, ε > 0. 2) for small ε > 0. Letting y → x we obtain εP f (x) ≤ ε lim inf P f (y) + o(ε). y→x Thus, P f (x) ≤ lim inf y→x P f (y) holds for all x ∈ E. Similarly, changing the roles of x and y we obtain P f (y) ≥ lim supx→y P f (x) for any y ∈ E. Therefore, P f is continuous. (2) To prove the existence of a kernel, it suffices to prove that for any A ∈ B with µ(A) = 0 we have P 1A ≡ 0.

1) Note that PT f (x) = E{Rf (YT )} = E{Rf (XT + e)}. We have p p−1 |PT f (x)|p ≤ E|f |p (XT +e) ER p−1 p = PT {|f |p (e+·)}(x) ER p−1 p−1 . Similarly, for positive f , PT log f (x) = E{R log f (XT + e)} ≤ log Ef (XT + e) + E(R log R) = log PT {f (e + ·)}(x) + E(R log R). (2) Noting that PT f (x) = E Rε f (X ε (T )) obtain 0= d E Rε f (XT + εe) dε ε=0 = E Rε f (XT + εe) , we = PT (∇e f )(x) − E f (XT )NT , d provided R0 = 1 and NT := − dε Rε |ε=0 exists in L1 (P). 4 Harnack inequalities and applications In this section we consider the Harnack and shift Harnack inequalities for a bounded linear operator and applications.

0 . Since µ0 is P -invariant, we have gP ∗ 1 dµ0 = g dµ0 , g ∈ Bb (E). e. e. x ∈ E the measure P ∗ (x, ·) is a probability measure. On the other hand, since µ1 is P -invariant, we have (P ∗ f )g dµ0 = E f P g dµ0 = E = P g dµ1 E f g dµ0 , g ∈ Bb (E). e. Therefore, P∗ E 1 dµ0 = f +1 E 1 dµ0 = f +1 1 E P ∗f +1 dµ0 . s. s. e. x. t. s. s. since f is a probability density function. 1) to f = n ∧ Φ−1 p(x, ·) p(y, ·) and letting n → ∞, we obtain the desired inequality. (5) Let rΦ−1 (r) be convex for r ≥ 0.

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