Onsager–Machlup function

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The Onsager–Machlup function is a function that summarizes the dynamics of a continuous stochastic process. It is used to define a probability density for a stochastic process, and it is similar to the Lagrangian of a dynamical system. It is named after Lars Onsager and S. Machlup who were the first to consider such probability densities.[1]

The dynamics of a continuous stochastic process X from time t = 0 to t = T in one dimension, satisfying a stochastic differential equation

 dX_t = b(X_t)\,dt + \sigma(X_t)\,dW_t

where W is a Wiener process, can in approximation be described by the probability density function of its value xi at a finite number of points in time ti:

 p(x_1,\ldots,x_n) = \left( \prod^{n-1}_{i=1} \frac{1}{\sqrt{2\pi\sigma(x_i)^2\Delta t_i}} \right) \exp\left(-\sum^{n-1}_{i=1} L\left(x_i,\frac{x_{i+1}-x_i}{\Delta t_i}\right) \, \Delta t_i \right)

where

 L(x,v) = \frac{1}{2}\left(\frac{v - b(x)}{\sigma}\right)^2

and Δti = ti+1ti > 0, t1 = 0 and tn = T. A similar approximation is possible for processes in higher dimensions. The approximation is more accurate for smaller time step sizes Δti, but in the limit Δti → 0 the probability density function becomes ill defined, one reason being that the product of terms

\frac{1}{\sqrt{2\pi\sigma(x_i)^2\Delta t_i}}

diverges to infinity. In order to nevertheless define a density for the continuous stochastic process X, ratios of probabilities of X lying within a small distance ε from smooth curves φ1 and φ2 are considered:[2]

\frac{P\left( \left |X_t - \varphi_1(t) \right| \leq \varepsilon \text{ for every }t\in[0,T] \right)}{P\left( \left |X_t - \varphi_2(t) \right | \leq \varepsilon \text{ for every }t\in[0,T] \right)} \to \exp\left(-\int^T_0 L \left (\varphi_1(t),\dot{\varphi}_1(t) \right ) \, dt + \int^T_0 L \left (\varphi_2(t),\dot{\varphi}_2(t) \right) \, dt \right)

as ε → 0, where L is the Onsager–Machlup function.

Definition[edit]

Consider a d-dimensional Riemannian manifold M and a diffusion process X = {Xt : 0 ≤ tT} on M with infinitesimal generator 1/2ΔM + b, where ΔM is the Laplace–Beltrami operator and b is a vector field. For any two smooth curves φ1, φ2 : [0, T] → M,

\lim_{\varepsilon\downarrow0} \frac{P\left( \rho(X_t,\varphi_1(t)) \leq \varepsilon \text{ for every }t\in[0,T] \right)}{P\left( \rho(X_t,\varphi_2(t)) \leq \varepsilon \text{ for every }t\in[0,T] \right)} = \exp\left( -\int^T_0 L \left (\varphi_1(t),\dot{\varphi}_1(t) \right ) \, dt  +\int^T_0 L \left (\varphi_2(t),\dot{\varphi}_2(t) \right ) \, dt  \right)

where ρ is the Riemannian distance, \scriptstyle \dot{\varphi}_1, \dot{\varphi}_2 denote the first derivatives of φ1, φ2, and L is called the Onsager–Machlup function.

The Onsager–Machlup function is given by[3][4][5]

 L(x,v) = \tfrac{1}{2}\|v-b(x)\|_x^2 +\tfrac{1}{2}\operatorname{div}\, b(x) - \tfrac{1}{12}R(x),

where || ⋅ ||x is the Riemannian norm in the tangent space Tx(M) at x, div b(x) is the divergence of b at x, and R(x) is the scalar curvature at x.

Examples[edit]

The following examples give explicit expressions for the Onsager–Machlup function of a continuous stochastic processes.

Wiener process on the real line[edit]

The Onsager–Machlup function of a Wiener process on the real line R is given by[6]

L(x,v)=\tfrac{1}{2}|v|^2.

Diffusion processes with constant diffusion coefficient on Euclidean space[edit]

The Onsager–Machlup function in the one-dimensional case with constant diffusion coefficient σ is given by[7]

L(x,v)=\frac{1}{2}\left|\frac{v-b(x)}{\sigma}\right|^2 + \frac{1}{2}\frac{db}{dx}(x).

In the d-dimensional case, with σ equal to the unit matrix, it is given by[8]

 L(x,v)=\frac{1}{2}\|v-b(x)\|^2 + \frac{1}{2}(\operatorname{div}\, b)(x),

where || ⋅ || is the Euclidean norm and

(\operatorname{div}\, b)(x) = \sum_{i=1}^d \frac{1}{2}\frac{\partial}{\partial x_i} b_i(x).

Generalizations[edit]

Generalizations have been obtained by weakening the differentiability condition on the curve φ.[9] Rather than taking the maximum distance between the stochastic process and the curve over a time interval, other conditions have been considered such as distances based on completely convex norms[10] and Hölder, Besov and Sobolev type norms.[11]

Applications[edit]

The Onsager–Machlup function can be used for purposes of reweighting and sampling trajectories,[12] as well as for determining the most probable trajectory of a diffusion process.[13][14]

See also[edit]

References[edit]

  1. ^ Onsager, L. and Machlup, S. (1953)
  2. ^ Stratonovich, R. (1971)
  3. ^ Takahashi, Y. and Watanabe, S. (1980)
  4. ^ Fujita, T. and Kotani, S. (1982)
  5. ^ Wittich, Olaf
  6. ^ Ikeda, N. and Watanabe, S. (1980), Chapter VI, Section 9
  7. ^ Dürr, D. and Bach, A. (1978)
  8. ^ Ikeda, N. and Watanabe, S. (1980), Chapter VI, Section 9
  9. ^ Zeitouni, O. (1989)
  10. ^ Shepp, L. and Zeitouni, O. (1993)
  11. ^ Capitaine, M. (1995)
  12. ^ Adib, A.B. (2008).
  13. ^ Adib, A.B. (2008).
  14. ^ Dürr, D. and Bach, A. (1978).

Bibliography[edit]

  • Adib, A.B. (2008). "Stochastic actions for diffusive dynamics: Reweighting, sampling, and minimization". J. Phys. Chem. B 112: 5910–5916. doi:10.1021/jp0751458. 
  • Capitaine, M. (1995). "Onsager–Machlup functional for some smooth norms on Wiener space". Probab. Theory Relat. Fields 102: 189–201. doi:10.1007/bf01213388. 
  • Dürr, D. and Bach, A. (1978). "The Onsager–Machlup function as Lagrangian for the most probable path of a diffusion process". Commun. Math. Phys. 60: 153–170. doi:10.1007/bf01609446. 
  • Fujita, T. and Kotani, S. (1982). "The Onsager–Machlup function for diffusion processes". J. Math. Kyoto Univ. 22: 115–130. 
  • Ikeda, N. and Watanabe, S. (1980). Stochastic differential equations and diffusion processes. Kodansha-John Wiley. 
  • Onsager, L. and Machlup, S. (1953). "Fluctuations and Irreversible Processes". Physical Review 91 (6): 1505–1512. doi:10.1103/physrev.91.1505. 
  • Shepp, L. and Zeitouni, O. (1993). "Exponential estimates for convex norms and some applications". Progress in Probability (Berlin. Birkhauser-Verlag) 32: 203–215. doi:10.1007/978-3-0348-8555-3_11. 
  • Stratonovich, R. (1971). "On the probability functional of diffusion processes". Select. Transl. in Math. Stat. Prob. 10: 273–286. 
  • Takahashi, Y. and Watanabe, S. (1980). "The probability functionals (Onsager–Machlup functions) of diffusion processes". Springer Lecture Notes in Math. 851: 432–463. 
  • Wittich, Olaf. The Onsager–Machlup Functional Revisited. 
  • Zeitouni, O. (1989). "On the Onsager–Machlup functional of diffusion processes around non C2 curves". Annals of Probability 17 (3): 1037–1054. doi:10.1214/aop/1176991255. 

External links[edit]