arXiv subjects

Iain Souttar

Publications and source records attributed to Iain Souttar.

3 recordsLinked to original sources

arXiv2024

Conditions for uniform in time convergence: applications to averaging, numerical discretisations and mean-field systems

We establish general conditions under which there exists uniform in time convergence between a stochastic process and its approximated system. These standardised conditions consist of a local in time estimate between the original and the approximated process as well as of a contraction property for one of the processes and a uniform control for the other one. Specifically, the results we present provide global in time error bounds for multiscale methods and numerical discretisations as well as uniform in time propagation of chaos bounds for mean-field particle systems. We provide a general method of proof which can be applied to many types of approximation. In all three scenarios, examples where the joint conditions are verified and uniform in time convergence is achieved are given.

math.PR↗

arXiv2024

A Multiscale Perspective on Maximum Marginal Likelihood Estimation

In this paper, we provide a multiscale perspective on the problem of maximum marginal likelihood estimation. We consider and analyse a diffusion-based maximum marginal likelihood estimation scheme using ideas from multiscale dynamics. Our perspective is based on stochastic averaging; we make an explicit connection between ideas in applied probability and parameter inference in computational statistics. In particular, we consider a general class of coupled Langevin diffusions for joint inference of latent variables and parameters in statistical models, where the latent variables are sampled from a fast Langevin process (which acts as a sampler), and the parameters are updated using a slow Langevin process (which acts as an optimiser). We show that the resulting system of stochastic differential equations (SDEs) can be viewed as a two-time scale system. To demonstrate the utility of such a perspective, we show that the \textit{averaged} parameter dynamics obtained in the limit of scale separation can be used to estimate the optimal parameter, within the strongly convex setting. We do this by using recent uniform-in-time non-asymptotic averaging bounds. Finally, we conclude by showing that the slow-fast algorithm we consider here, termed Slow-Fast Langevin Algorithm, performs on par with state-of-the-art methods on a variety of examples. We believe that the stochastic averaging approach we provide in this paper enables us to look at these algorithms from a fresh angle, as well as unlocking the path to develop and analyse new methods using well-established averaging principles.

stat.CO↗

arXiv2024

Poisson Equations with locally-Lipschitz coefficients and Uniform in Time Averaging for Stochastic Differential Equations via Strong Exponential Stability

We study averaging for Stochastic Differential Equations (SDEs) and Poisson equations. We succeed in obtaining a uniform in time (UiT) averaging result, with a rate, for fully coupled SDE models with super-linearly growing coefficients. This is the main result of this paper and is, to the best of our knowledge, the first UiT multiscale result with a rate. Very few UiT averaging results exist in the literature, and they almost exclusively apply to multiscale systems of Ordinary Differential Equations. Among these few, none of those we are aware of comes with a rate of convergence. The UiT nature of this result and the rate of convergence given by the main theorem, make it important as theoretical underpinning for a range of applications, such as applications to statistical methodology, molecular dynamics etc. Key to obtaining both our UiT averaging result and to enable dealing with the super-linear growth of the coefficients is conquering exponential decay in time of the space-derivatives of appropriate Markov semigroups. We refer to this property as being Strongly Exponentially Stable (SES). The analytic approach to proving averaging results we take requires studying a family of Poisson problems associated with the generator of the (fast component of the) SDE dynamics. The study of Poisson equations in non-compact state space is notoriously difficult, with current literature mostly covering the case when the coefficients of the Partial Differential Equation (PDE) are either bounded or satisfy linear growth assumptions. In this paper we treat Poisson equations on non-compact state spaces for coefficients that can grow super-linearly. We demonstrate how SES can be employed not only to prove the UiT result for the slow-fast system but also to overcome some of the technical hurdles in the analysis of Poisson problems, which is of independent interest as well.

math.PR↗