Publications

Search or filter publications

Filter by type:

Filter by publication type

Filter by year:

to

Results

  • Showing results for:
  • Reset all filters

Search results

  • JOURNAL ARTICLE
    Crisan D, McMurray E, 2018,

    Smoothing properties of McKean–Vlasov SDEs

    , Probability Theory and Related Fields, Vol: 171, Pages: 97-148, ISSN: 0178-8051

    © 2017, The Author(s). In this article, we develop integration by parts formulae on Wiener space for solutions of SDEs with general McKean–Vlasov interaction and uniformly elliptic coefficients. These integration by parts formulae hold both for derivatives with respect to a real variable and derivatives with respect to a measure understood in the sense of Lions. They allows us to prove the existence of a classical solution to a related PDE with irregular terminal condition. We also develop bounds for the derivatives of the density of the solutions of McKean–Vlasov SDEs.
  • JOURNAL ARTICLE
    Crisan D, Míguez J, 2018,

    Nested particle filters for online parameter estimation in discrete-time state-space Markov models

    , Bernoulli, Vol: 24, Pages: 2429-2460, ISSN: 1350-7265

    © 2018 ISI/BS. We address the problem of approximating the posterior probability distribution of the fixed parameters of a state-space dynamical system using a sequential Monte Carlo method. The proposed approach relies on a nested structure that employs two layers of particle filters to approximate the posterior probability measure of the static parameters and the dynamic state variables of the system of interest, in a vein similar to the recent “sequential Monte Carlo square” (SMC2) algorithm. However, unlike the SMC2scheme, the proposed technique operates in a purely recursive manner. In particular, the computational complexity of the recursive steps of the method introduced herein is constant over time. We analyse the approximation of integrals of real bounded functions with respect to the posterior distribution of the system parameters computed via the proposed scheme. As a result, we prove, under regularity assumptions, that the approximation errors vanish asymptotically in Lp(p ≥ 1) with convergence rate proportional to1N+1M, where N is the number of Monte Carlo samples in the parameter space and N × M is the number of samples in the state space. This result also holds for the approximation of the joint posterior distribution of the parameters and the state variables. We discuss the relationship between the SMC2algorithm and the new recursive method and present a simple example in order to illustrate some of the theoretical findings with computer simulations.
  • JOURNAL ARTICLE
    Davis M, Obłój J, Siorpaes P, 2018,

    Pathwise stochastic calculus with local times

    , Annales de l'Institut Henri Poincaré (B) Probabilités et Statistiques, Vol: 54, Pages: 1-21, ISSN: 0246-0203

    We study a notion of local time for a continuous path, defined as a limit ofsuitable discrete quantities along a general sequence of partitions of the timeinterval. Our approach subsumes other existing definitions and agrees with theusual (stochastic) local times a.s. for paths of a continuous semimartingale.We establish pathwise version of the It\^o-Tanaka, change of variables andchange of time formulae. We provide equivalent conditions for existence ofpathwise local time. Finally, we study in detail how the limiting objects, thequadratic variation and the local time, depend on the choice of partitions. Inparticular, we show that an arbitrary given non-decreasing process can beachieved a.s. by the pathwise quadratic variation of a standard Brownian motionfor a suitable sequence of (random) partitions; however, such degeneratebehavior is excluded when the partitions are constructed from stopping times.
  • CONFERENCE PAPER
    Dutordoir V, Salimbeni H, Deisenroth M, Hensman Jet al., 2018,

    Gaussian Process Conditional Density Estimation

    Conditional Density Estimation (CDE) models deal with estimating conditionaldistributions. The conditions imposed on the distribution are the inputs of themodel. CDE is a challenging task as there is a fundamental trade-off betweenmodel complexity, representational capacity and overfitting. In this work, wepropose to extend the model's input with latent variables and use Gaussianprocesses (GP) to map this augmented input onto samples from the conditionaldistribution. Our Bayesian approach allows for the modeling of small datasets,but we also provide the machinery for it to be applied to big data usingstochastic variational inference. Our approach can be used to model densitieseven in sparse data regions, and allows for sharing learned structure betweenconditions. We illustrate the effectiveness and wide-reaching applicability ofour model on a variety of real-world problems, such as spatio-temporal densityestimation of taxi drop-offs, non-Gaussian noise modeling, and few-shotlearning on omniglot images.
  • JOURNAL ARTICLE
    Gulisashvili A, Horvath B, Jacquier A, 2018,

    Mass at zero in the uncorrelated SABR model and implied volatility asymptotics

    , Quantitative Finance, Vol: 18, Pages: 1753-1765, ISSN: 1469-7688

    We study the mass at the origin in the uncorrelated SABR stochasticvolatility model, and derive several tractable expressions, in particular whentime becomes small or large. As an application--in fact the original motivationfor this paper--we derive small-strike expansions for the implied volatilitywhen the maturity becomes short or large. These formulae, by definitionarbitrage free, allow us to quantify the impact of the mass at zero on existingimplied volatility approximations, and in particular how correct/erroneousthese approximations become.
  • JOURNAL ARTICLE
    Noven RC, Veraart AED, Gandy A, 2018,

    A latent trawl process model for extreme values

    , JOURNAL OF ENERGY MARKETS, Vol: 11, Pages: 1-24, ISSN: 1756-3607
  • JOURNAL ARTICLE
    Olofsson S, Deisenroth MP, Misener R, 2018,

    Design of Experiments for Model Discrimination using Gaussian Process Surrogate Models

    , Vol: 44, Pages: 847-852, ISSN: 1570-7946

    © 2018 Elsevier B.V. Given rival mathematical models and an initial experimental data set, optimal design of experiments for model discrimination discards inaccurate models. Model discrimination is fundamentally about finding out how systems work. Not knowing how a particular system works, or having several rivalling models to predict the behaviour of the system, makes controlling and optimising the system more difficult. The most common way to perform model discrimination is by maximising the pairwise squared difference between model predictions, weighted by measurement noise and model uncertainty resulting from uncertainty in the fitted model parameters. The model uncertainty for analytical model functions is computed using gradient information. We develop a novel method where we replace the black-box models with Gaussian process surrogate models. Using the surrogate models, we are able to approximately marginalise out the model parameters, yielding the model uncertainty. Results show the surrogate model method working for model discrimination for classical test instances.
  • CONFERENCE PAPER
    Salimbeni H, Cheng C-A, Boots B, Deisenroth MPet al., 2018,

    Orthogonally Decoupled Variational Gaussian Processes.
  • JOURNAL ARTICLE
    Abdulle A, Pavliotis GA, Vaes U, 2017,

    Spectral Methods for Multiscale Stochastic Differential Equations

    , SIAM-ASA JOURNAL ON UNCERTAINTY QUANTIFICATION, Vol: 5, Pages: 720-761, ISSN: 2166-2525
  • CONFERENCE PAPER
    Amiri MM, Gunduz D, 2017,

    Decentralized Caching and Coded Delivery over Gaussian Broadcast Channels

    , IEEE International Symposium on Information Theory (ISIT), Publisher: IEEE, Pages: 2785-2789

This data is extracted from the Web of Science and reproduced under a licence from Thomson Reuters. You may not copy or re-distribute this data in whole or in part without the written consent of the Science business of Thomson Reuters.

Request URL: http://wlsprd.imperial.ac.uk:80/respub/WEB-INF/jsp/search-t4-html.jsp Request URI: /respub/WEB-INF/jsp/search-t4-html.jsp Query String: id=961&limit=10&respub-action=search.html Current Millis: 1542478578999 Current Time: Sat Nov 17 18:16:18 GMT 2018