Download Analysis and Geometry of Markov Diffusion Operators by Dominique Bakry, Ivan Gentil, Michel Ledoux (auth.) PDF

By Dominique Bakry, Ivan Gentil, Michel Ledoux (auth.)

The current quantity is an intensive monograph at the analytic and geometric features of Markov diffusion operators. It makes a speciality of the geometric curvature houses of the underlying constitution with the intention to learn convergence to equilibrium, spectral bounds, useful inequalities corresponding to Poincaré, Sobolev or logarithmic Sobolev inequalities, and numerous bounds on recommendations of evolution equations. whilst, it covers a wide type of evolution and partial differential equations.

The e-book is meant to function an advent to the topic and to be obtainable for starting and complicated scientists and non-specialists. at the same time, it covers a variety of effects and methods from the early advancements within the mid-eighties to the newest achievements. As such, scholars and researchers drawn to the fashionable points of Markov diffusion operators and semigroups and their connections to analytic sensible inequalities, probabilistic convergence to equilibrium and geometric curvature will locate it particularly helpful. chosen chapters is additionally used for complicated classes at the topic.

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Extra resources for Analysis and Geometry of Markov Diffusion Operators

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From both the point of view of probability and analysis, these ergodic properties may be seen as “convergence to equilibrium”, the equilibrium being the invariant measure (whenever finite). The analysis of convergence to equilibrium plays an important role in many fields, and many of the methods and functional inequalities developed in this monograph will lead to (precise) quantitative bounds on the convergence to equilibrium. Determining the class of functions f such that Pt f (x) converges, and the sense in which it converges, strongly depends on the context.

A similar description is available in the discrete Markov chain setting (Sect. 9). This property is related to connexity of the state space as developed below in Sect. 7, p. 10, p. 157. Once we know that invariant functions are constant, there are only two possibilities: either μ(E) < ∞, and assuming that μ is a probability measure, P∞ f = 2 E f dμ, or μ(E) = ∞, and since 0 is the only constant in L (μ), P∞ f = 0. These two situations are represented by the Ornstein-Uhlenbeck semigroup and the standard heat semigroup in Rn (see Sect.

Xtk ) = E×···×E f (y1 , . . 3) × ptk−1 −tk−2 (yk−2 , dyk−1 ) · · · pt1 (x, dy1 ) for every, say, positive or bounded measurable function f on the product space E × · · · × E. When the distribution of such a k-dimensional vector (Xt1 , . . , Xtk ) is specified, it determines the distributions of each extracted lower dimensional vector (for example, the law of (Xt1 , Xt3 ) can be deduced from that of (Xt1 , Xt2 , Xt3 )). It is thus necessary that the system of finite-dimensional distributions be compatible, which is precisely the content of the Chapman-Kolmogorov equation.

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