25,868 research outputs found
Robust Classification of Functional and Quantitative Image Data Using Functional Mixed Models
This paper describes how to perform classification of complex, high-dimensional functional data using the functional mixed model (FMM) framework. The FMM relates a functional response to a set of predictors through functional fixed and random effects, which allows it to account for various factors and between-function correlations. Classification is performed through training the model treating class as one of the fixed effects, and then predicting on the test data using posterior predictive probabilities of class. Through a Bayesian scheme, we are able to adjust for factors affecting both the functions and the class designations. While the method we present can be applied to any FMM-based method, we provide details for two specific Bayesian approaches: the Gaussian, wavelet-based functional mixed model (G-WFMM) and the robust, wavelet-based functional mixed model (R-WFMM). Both methods perform modeling in the wavelet space, which yields parsimonious representations for the functions, and can naturally adapt to local features and complex nonstationarities in the functions. The R-WFMM allows potentially heavier tails for features of the functions indexed by particular wavelet coefficients, leading to a down weighting of outliers that makes the method robust to outlying functions or regions of functions. The models are applied to a pancreatic cancer mass spectroscopy data set and compared with some other recently developed functional classification methods
Inference for reaction networks using the Linear Noise Approximation
We consider inference for the reaction rates in discretely observed networks
such as those found in models for systems biology, population ecology and
epidemics. Most such networks are neither slow enough nor small enough for
inference via the true state-dependent Markov jump process to be feasible.
Typically, inference is conducted by approximating the dynamics through an
ordinary differential equation (ODE), or a stochastic differential equation
(SDE). The former ignores the stochasticity in the true model, and can lead to
inaccurate inferences. The latter is more accurate but is harder to implement
as the transition density of the SDE model is generally unknown. The Linear
Noise Approximation (LNA) is a first order Taylor expansion of the
approximating SDE about a deterministic solution and can be viewed as a
compromise between the ODE and SDE models. It is a stochastic model, but
discrete time transition probabilities for the LNA are available through the
solution of a series of ordinary differential equations. We describe how a
restarting LNA can be efficiently used to perform inference for a general class
of reaction networks; evaluate the accuracy of such an approach; and show how
and when this approach is either statistically or computationally more
efficient than ODE or SDE methods. We apply the LNA to analyse Google Flu
Trends data from the North and South Islands of New Zealand, and are able to
obtain more accurate short-term forecasts of new flu cases than another
recently proposed method, although at a greater computational cost
Predictive biometrics: A review and analysis of predicting personal characteristics from biometric data
Interest in the exploitation of soft biometrics information has continued to develop over the last decade or so. In comparison with traditional biometrics, which focuses principally on person identification, the idea of soft biometrics processing is to study the utilisation of more general information regarding a system user, which is not necessarily unique. There are increasing indications that this type of data will have great value in providing complementary information for user authentication. However, the authors have also seen a growing interest in broadening the predictive capabilities of biometric data, encompassing both easily definable characteristics such as subject age and, most recently, `higher level' characteristics such as emotional or mental states. This study will present a selective review of the predictive capabilities, in the widest sense, of biometric data processing, providing an analysis of the key issues still adequately to be addressed if this concept of predictive biometrics is to be fully exploited in the future
Embedding Population Dynamics Models in Inference
Increasing pressures on the environment are generating an ever-increasing
need to manage animal and plant populations sustainably, and to protect and
rebuild endangered populations. Effective management requires reliable
mathematical models, so that the effects of management action can be predicted,
and the uncertainty in these predictions quantified. These models must be able
to predict the response of populations to anthropogenic change, while handling
the major sources of uncertainty. We describe a simple ``building block''
approach to formulating discrete-time models. We show how to estimate the
parameters of such models from time series of data, and how to quantify
uncertainty in those estimates and in numbers of individuals of different types
in populations, using computer-intensive Bayesian methods. We also discuss
advantages and pitfalls of the approach, and give an example using the British
grey seal population.Comment: Published at http://dx.doi.org/10.1214/088342306000000673 in the
Statistical Science (http://www.imstat.org/sts/) by the Institute of
Mathematical Statistics (http://www.imstat.org
Empirical Formulation of Highway Traffic Flow Prediction Objective Function Based on Network Topology
Accurate Highway road predictions are necessary for timely decision making by the transport authorities. In this paper, we propose a traffic flow objective function for a highway road prediction model. The bi-directional flow function of individual roads is reported considering the net inflows and outflows by a topological breakdown of the highway network. Further, we optimise and compare the proposed objective function for constraints involved using stacked long short-term memory (LSTM) based recurrent neural network machine learning model considering different loss functions and training optimisation strategies. Finally, we report the best fitting machine learning model parameters for the proposed flow objective function for better prediction accuracy.Peer reviewe
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