By M. I. Freidlin, A. D. Wentzell

Asymptotical difficulties have regularly performed an enormous position in likelihood thought. In classical chance concept dealing commonly with sequences of self sufficient variables, theorems of the kind of legislation of enormous numbers, theorems of the kind of the imperative restrict theorem, and theorems on huge deviations represent a big a part of all investigations. lately, whilst random strategies became the most topic of analysis, asymptotic investigations have endured to playa significant function. we will be able to say that during the idea of random techniques such investigations play an excellent higher position than in classical likelihood concept, since it is outwardly most unlikely to procure uncomplicated precise formulation in difficulties attached with huge sessions of random tactics. Asymptotical investigations within the idea of random techniques contain result of the kinds of either the legislation of enormous numbers and the imperative restrict theorem and, long ago decade, theorems on huge deviations. in fact, some of these difficulties have got new features and new interpretations within the thought of random processes.

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**Extra info for Random Perturbations of Dynamical Systems**

**Example text**

2 ds. 12) ds. 1, we conclude that 47 §l. Zeroth Order Approximation Now we apply Ito's formula to the function IX~ - X t 12 and take the the mathematical expectation on both sides of the equality: M IX~ - Xt 12 = 2 {M(X! 2, It follows from the definition of X~ and X t that max IX! - Xs I ::; O~sSt It Ib(X:) 0 b(x s)Ids + [; max 'Is O"(X~) dw v,. 7) (t). 48 2. 8) imply the last assertion of the theorem. 1. We assumed that the coefficients satisfied a Lipschitz condition instead of continuity. However, we obtained a stronger result in that not only did we prove that converges to x" but we also obtain estimates ofthe rate of convergence.

For the sake of definiteness, let X~, t E [0, T], 6 > 0, be a family of random processes whose trajectories are, with probability one, continuous functions defined on [0, T] with values in Rr. As usual, we denote by CoT(R r) the space of such functions with the topology of uniform convergence. ' be the family of measures corresponding to the processes X~ in CoT(Rr). ' on CoT(R r) converges weakly to a measure Jl. (dx). 24 1. Random Perturbations for every continuous bounded functional f(x) on COT (Rr).

Random Perturbations A detailed exposition of the semigroup theory of Markov processes can be found in Dynkin's book [2]. We consider examples of Markov processes and their infinitesimal generators. FIRST EXAMPLE. JH be the collection of its subsets. A Markov process with such a phase space is called a Markov process with a finite number of states. With every such process there is associated a system of functions Pij(t) (i, j EX, t;:::; 0) satisfying the following conditions: (1) (2) (3) (4) Pij(t) 2 0 for i,j EX, t 2 0; LjEXPij(t) = 1; Pij(O) = 0 for i "# j, Pii(O) = 1 for i E X; Pij(s + t) = LkEXPik(t)Pkj(S), The transition function of the process can be expressed in terms of the functions Pij(t) in the following way: pet, x, n = L Pxit); YEf We shall only consider stochastically continuous processes with a finite number of states.