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    Briol F-X, Oates CJ, Girolami M, Osborne MA, Sejdinovic Det al.,

    Probabilistic Integration: A Role in Statistical Computation?

    , Statistical Science, ISSN: 0883-4237

    A research frontier has emerged in scientific computation, wherein numericalerror is regarded as a source of epistemic uncertainty that can be modelled.This raises several statistical challenges, including the design of statisticalmethods that enable the coherent propagation of probabilities through a(possibly deterministic) computational work-flow. This paper examines the casefor probabilistic numerical methods in routine statistical computation. Ourfocus is on numerical integration, where a probabilistic integrator is equippedwith a full distribution over its output that reflects the presence of anunknown numerical error. Our main technical contribution is to establish, forthe first time, rates of posterior contraction for these methods. These showthat probabilistic integrators can in principle enjoy the "best of bothworlds", leveraging the sampling efficiency of Monte Carlo methods whilstproviding a principled route to assess the impact of numerical error onscientific conclusions. Several substantial applications are provided forillustration and critical evaluation, including examples from statisticalmodelling, computer graphics and a computer model for an oil reservoir.

    Ellam L, Girolami M, Pavliotis GA, Wilson Aet al., 2018,

    Stochastic modelling of urban structure

    Barp A, Briol F-X, Kennedy AD, Girolami Met al., 2018,

    Geometry and dynamics for Markov chain Monte Carlo

    , Annual Review of Statistics and Its Application, Vol: 5, Pages: 451-471, ISSN: 2326-8298

    Markov Chain Monte Carlo methods have revolutionised mathematical computationand enabled statistical inference within many previously intractable models. Inthis context, Hamiltonian dynamics have been proposed as an efficient way ofbuilding chains which can explore probability densities efficiently. The methodemerges from physics and geometry and these links have been extensively studiedby a series of authors through the last thirty years. However, there iscurrently a gap between the intuitions and knowledge of users of themethodology and our deep understanding of these theoretical foundations. Theaim of this review is to provide a comprehensive introduction to the geometrictools used in Hamiltonian Monte Carlo at a level accessible to statisticians,machine learners and other users of the methodology with only a basicunderstanding of Monte Carlo methods. This will be complemented with somediscussion of the most recent advances in the field which we believe willbecome increasingly relevant to applied scientists.

    , 2017,

    On the sampling problem for Kernel quadrature

    , Pages: 949-968

    © 2017 by the author(s). The standard Kernel Quadrature method for numerical integration with random point sets (also called Bayesian Monte Carlo) is known to converge in root mean square error at a rate determined by the ratio s/d, where s and d encode the smoothness and dimension of the integrand. However, an empirical investigation reveals that the rate constant C is highly sensitive to the distribution of the random points. In contrast to standard Monte Carlo integration, for which optimal importance sampling is well-understood, the sampling distribution that minimises C for Kernel Quadrature does not admit a closed form. This paper argues that the practical choice of sampling distribution is an important open problem. One solution is considered; a novel automatic approach based on adaptive tempering and sequential Monte Carlo. Empirical results demonstrate a dramatic reduction in integration error of up to 4 orders of magnitude can be achieved with the proposed method.

    Oates CJ, Niederer S, Lee A, Briol F-X, Girolami Met al., 2017,

    Probabilistic Models for Integration Error in the Assessment of Functional Cardiac Models

    , 31st Conference on Neural Information Processing Systems (NIPS), Publisher: NEURAL INFORMATION PROCESSING SYSTEMS (NIPS), ISSN: 1049-5258
    Ellam L, Strathmann H, Girolami M, Murray Iet al., 2017,

    A determinant-free method to simulate the parameters of large Gaussian fields

    , STAT, Vol: 6, Pages: 271-281, ISSN: 2049-1573
    Chkrebtii OA, Campbell DA, Calderhead B, Girolami MAet al., 2016,

    o Rejoinder

    , BAYESIAN ANALYSIS, Vol: 11, Pages: 1295-1299, ISSN: 1931-6690
    Chkrebtii OA, Campbell DA, Calderhead B, Girolami MAet al., 2016,

    Bayesian Solution Uncertainty Quantification for Differential Equations

    , BAYESIAN ANALYSIS, Vol: 11, Pages: 1239-1267, ISSN: 1931-6690
    , 2016,

    A Bayesian approach to multiscale inverse problems with on-the-fly scale determination

    , Journal of Computational Physics, Vol: 326, Pages: 115-140, ISSN: 0021-9991

    © 2016 Elsevier Inc. A Bayesian computational approach is presented to provide a multi-resolution estimate of an unknown spatially varying parameter from indirect measurement data. In particular, we are interested in spatially varying parameters with multiscale characteristics. In our work, we consider the challenge of not knowing the characteristic length scale(s) of the unknown a priori, and present an algorithm for on-the-fly scale determination. Our approach is based on representing the spatial field with a wavelet expansion. Wavelet basis functions are hierarchically structured, localized in both spatial and frequency domains and tend to provide sparse representations in that a large number of wavelet coefficients are approximately zero. For these reasons, wavelet bases are suitable for representing permeability fields with non-trivial correlation structures. Moreover, the intra-scale correlations between wavelet coefficients form a quadtree, and this structure is exploited to identify additional basis functions to refine the model. Bayesian inference is performed using a sequential Monte Carlo (SMC) sampler with a Markov Chain Monte Carlo (MCMC) transition kernel. The SMC sampler is used to move between posterior densities defined on different scales, thereby providing a computationally efficient method for adaptive refinement of the wavelet representation. We gain insight from the marginal likelihoods, by computing Bayes factors, for model comparison and model selection. The marginal likelihoods provide a termination criterion for our scale determination algorithm. The Bayesian computational approach is rather general and applicable to several inverse problems concerning the estimation of a spatially varying parameter. The approach is demonstrated with permeability estimation for groundwater flow using pressure sensor measurements.

    Briol F-X, Cockayne J, Teymur O, 2016,

    Contributed Discussion on Article by Chkrebtii, Campbell, Calderhead, and Girolami

    , Bayesian Analysis, Vol: 11, Pages: 1285-1293, ISSN: 1931-6690

    We commend the authors for an exciting paper which provides a strongcontribution to the emerging field of probabilistic numerics (PN). Below, we discuss aspects of prior modelling which need to be considered thoroughly in future work

    Epstein M, Calderhead B, Girolami MA, Sivilotti LGet al., 2016,

    Bayesian Statistical Inference in Ion-Channel Models with Exact Missed Event Correction

    , BIOPHYSICAL JOURNAL, Vol: 111, Pages: 333-348, ISSN: 0006-3495
    Briol F-X, Oates CJ, Girolami M, Osborne MAet al., 2015,

    Frank-Wolfe Bayesian Quadrature: Probabilistic Integration with Theoretical Guarantees

    , 29th Annual Conference on Neural Information Processing Systems (NIPS), Publisher: NEURAL INFORMATION PROCESSING SYSTEMS (NIPS), ISSN: 1049-5258
    Girolami MA, 2014,

    Big Bayes Stories: A Collection of Vignettes

    , STATISTICAL SCIENCE, Vol: 29, Pages: 97-97, ISSN: 0883-4237
    Stathopoulos V, Girolami MA, 2013,

    Markov chain Monte Carlo inference for Markov jump processes via the linear noise approximation

    Oates CJ, Cockayne J, Briol F-X, Girolami Met al.,

    Convergence Rates for a Class of Estimators Based on Stein's Method

    Gradient information on the sampling distribution can be used to reduce thevariance of Monte Carlo estimators via Stein's method. An important applicationis that of estimating an expectation of a test function along the sample pathof a Markov chain, where gradient information enables convergence rateimprovement at the cost of a linear system which must be solved. Thecontribution of this paper is to establish theoretical bounds on convergencerates for a class of estimators based on Stein's method. Our analysis accountsfor (i) the degree of smoothness of the sampling distribution and testfunction, (ii) the dimension of the state space, and (iii) the case ofnon-independent samples arising from a Markov chain. These results provideinsight into the rapid convergence of gradient-based estimators observed forlow-dimensional problems, as well as clarifying a curse-of-dimension thatappears inherent to such methods.

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