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Exact Ornstein–Uhlenbeck momentum transition and covariance
Statement
For the O subproblem over a step , with measurable at the start of the step and Brownian increments independent of the starting sigma algebra, where is independent of the starting sigma algebra. In particular, conditionally on , the transition is Gaussian with mean and covariance .
Facts & Assumptions
Given: The model data in Underdamped Langevin equation and canonical Gibbs density, a deterministic step , and the O subproblem.
Underdamped Langevin equation and canonical Gibbs density: is symmetric positive definite and the O subproblem has friction and noise covariance .
Deterministic Ito integrals are Gaussian: a finite vector of deterministic It^o integrals against Brownian increments is jointly centered Gaussian, with covariance entries given by the integrals of products of their deterministic integrands; future increments are independent of the starting sigma algebra.
Multidimensional Ito formula for Brownian-driven processes: the It^o product rule applies to a continuously differentiable deterministic scalar factor times a Brownian It^o process.
A non-negative operator has a unique non-negative square root: has a unique symmetric positive square root.
The Axiom of Choice: AC is inherited from the deterministic It^o integral and Gaussian-law interfaces [F2]; no further choice is used.
Proof
Given: The assumptions above.
Put . Multiplying the O equation by the deterministic integrating factor and applying [F3] componentwise gives . Integrating from to and multiplying by yields [F1, F2, F3, A1, given, algebra] The integrand is deterministic and square-integrable. By [F2] the integral is centered Gaussian, independent of the starting sigma algebra, with covariance
By [F4], the displayed covariance has square root . A centered Gaussian vector with this covariance has the same law as that matrix times ; [F2] gives independence from the starting sigma algebra. This proves the exact transition and conditional moments.
Depends on
Used by
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Sources
- Jean-Noël Roux, Stéphane Rodts, and Gabriel Stoltz, Introduction à la physique statistique et à la physique quantique, §5.2.2, pp. 85–87 (standard reference, not scraped)
- Benedict Leimkuhler and Charles Matthews, Rational Construction of Stochastic Numerical Methods for Molecular Sampling, §2 and Appendix (standard reference, not scraped)