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  • JOURNAL ARTICLE
    Rivera-Rubio J, Alexiou I, Bharath AA, 2015,

    Appearance-based indoor localization: A comparison of patch descriptor performance

    , PATTERN RECOGNITION LETTERS, Vol: 66, Pages: 109-117, ISSN: 0167-8655
  • CONFERENCE PAPER
    Rivera-Rubio J, Alexiou I, Bharath AA, 2015,

    Indoor Localisation with Regression Networks and Place Cell Models.

    , Publisher: BMVA Press, Pages: 147.1-147.1
  • CONFERENCE PAPER
    Wahlstrom N, Schon TB, Deisenroth MP, 2015,

    Learning Deep Dynamical Models From Image Pixels

    , 17th IFAC Symposium on System Identification, SYSID 2015
  • JOURNAL ARTICLE
    Liepe J, Kirk P, Filippi S, Toni T, Barnes CP, Stumpf MPHet al., 2014,

    A framework for parameter estimation and model selection from experimental data in systems biology using approximate Bayesian computation

    , NATURE PROTOCOLS, Vol: 9, Pages: 439-456, ISSN: 1754-2189
  • JOURNAL ARTICLE
    Filippi S, Barnes CP, Cornebise J, Stumpf MPHet al., 2013,

    On optimality of kernels for approximate Bayesian computation using sequential Monte Carlo

    , STATISTICAL APPLICATIONS IN GENETICS AND MOLECULAR BIOLOGY, Vol: 12, ISSN: 2194-6302
  • JOURNAL ARTICLE
    Silk D, Filippi S, Stumpf MPH, 2013,

    Optimizing threshold-schedules for sequential approximate Bayesian computation: applications to molecular systems

    , STATISTICAL APPLICATIONS IN GENETICS AND MOLECULAR BIOLOGY, Vol: 12, Pages: 603-618, ISSN: 2194-6302
  • JOURNAL ARTICLE
    Silk D, Filippi S, Stumpf MPH, 2013,

    Optimizing threshold-schedules for sequential approximate Bayesian computation: applications to molecular systems

    , Statistical Applications in Genetics and Molecular Biology, Vol: 12, Pages: 603-618, ISSN: 2194-6302

    The likelihood–free sequential Approximate Bayesian Computation (ABC) algorithms are increasingly popular inference tools for complex biological models. Such algorithms proceed by constructing a succession of probability distributions over the parameter space conditional upon the simulated data lying in an ε–ball around the observed data, for decreasing values of the threshold ε. While in theory, the distributions (starting from a suitably defined prior) will converge towards the unknown posterior as ε tends to zero, the exact sequence of thresholds can impact upon the computational efficiency and success of a particular application. In particular, we show here that the current preferred method of choosing thresholds as a pre-determined quantile of the distances between simulated and observed data from the previous population, can lead to the inferred posterior distribution being very different to the true posterior. Threshold selection thus remains an important challenge. Here we propose that the threshold–acceptance rate curve may be used to determine threshold schedules that avoid local optima, while balancing the need to minimise the threshold with computational efficiency. Furthermore, we provide an algorithm based upon the unscented transform, that enables the threshold–acceptance rate curve to be efficiently predicted in the case of deterministic and stochastic state space models.

  • JOURNAL ARTICLE
    Barnes C, Filippi S, Stumpf MPH, Thorne Tet al., 2012,

    Considerate approaches to achieving sufficiency for ABC model selection

    , Statistics and Computing, Vol: 22, Pages: 1181-1197, ISSN: 0960-3174

    For nearly any challenging scientific problemevaluation of the likelihood is problematic if not impossible.Approximate Bayesian computation (ABC) allowsus to employ the whole Bayesian formalism to problemswhere we can use simulations from a model, but cannotevaluate the likelihood directly. When summary statistics ofreal and simulated data are compared—rather than the datadirectly—information is lost, unless the summary statisticsare sufficient. Sufficient statistics are, however, not commonbut without them statistical inference in ABC inferencesare to be considered with caution. Previously other authorshave attempted to combine different statistics in order toconstruct (approximately) sufficient statistics using searchand information heuristics. Here we employ an informationtheoreticalframework that can be used to construct appropriate(approximately sufficient) statistics by combining differentstatistics until the loss of information is minimized.We start from a potentially large number of different statisticsand choose the smallest set that captures (nearly) thesame information as the complete set. We then demonstratethat such sets of statistics can be constructed for both parameterestimation and model selection problems, and we applyour approach to a range of illustrative and real-world modelselection problems.

  • JOURNAL ARTICLE
    Barnes CP, Filippi S, Stumpf MPH, Thorne Tet al., 2012,

    Considerate approaches to constructing summary statistics for ABC model selection

    , STATISTICS AND COMPUTING, Vol: 22, Pages: 1181-1197, ISSN: 0960-3174
  • JOURNAL ARTICLE
    Filippi S, Cappe O, Garivier A, 2011,

    Optimally Sensing a Single Channel Without Prior Information: The Tiling Algorithm and Regret Bounds

    , IEEE JOURNAL OF SELECTED TOPICS IN SIGNAL PROCESSING, Vol: 5, Pages: 68-76, ISSN: 1932-4553

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