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pro vyhledávání: '"Calderhead, Ben"'
We propose a novel algorithm for efficiently computing a sparse directed adjacency matrix from a group of time series following a causal graph process. Our solution is scalable for both dense and sparse graphs and automatically selects the LASSO coef
Externí odkaz:
http://arxiv.org/abs/1906.04479
The econometric challenge of finding sparse mean reverting portfolios based on a subset of a large number of assets is well known. Many current state-of-the-art approaches fall into the field of co-integration theory, where the problem is phrased in
Externí odkaz:
http://arxiv.org/abs/1905.05841
We propose a heterogeneous simultaneous graphical dynamic linear model (H-SGDLM), which extends the standard SGDLM framework to incorporate a heterogeneous autoregressive realised volatility (HAR-RV) model. This novel approach creates a GPU-scalable
Externí odkaz:
http://arxiv.org/abs/1904.08153
Autor:
Schwedes, Tobias, Calderhead, Ben
Quasi-Monte Carlo (QMC) methods for estimating integrals are attractive since the resulting estimators typically converge at a faster rate than pseudo-random Monte Carlo. However, they can be difficult to set up on arbitrary posterior densities withi
Externí odkaz:
http://arxiv.org/abs/1807.00070
Publikováno v:
Advances in Neural Information Processing Systems 31 (2018) pp. 7244-7253
We introduce a family of implicit probabilistic integrators for initial value problems (IVPs), taking as a starting point the multistep Adams-Moulton method. The implicit construction allows for dynamic feedback from the forthcoming time-step, in con
Externí odkaz:
http://arxiv.org/abs/1805.07970
Publikováno v:
Advances in Neural Information Processing Systems 29 (2016) pp. 4321-4328
We present a derivation and theoretical investigation of the Adams-Bashforth and Adams-Moulton family of linear multistep methods for solving ordinary differential equations, starting from a Gaussian process (GP) framework. In the limit, this formula
Externí odkaz:
http://arxiv.org/abs/1610.08417
Autor:
Calderhead, Ben
This thesis presents novel Markov chain Monte Carlo methodology that exploits the natural representation of a statistical model as a Riemannian manifold. The methods developed provide generalisations of the Metropolis-adjusted Langevin algorithm and
Externí odkaz:
http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.547890
Autor:
Calderhead, Ben.
Publikováno v:
Connect to e-thesis..
Thesis (MSc(R)) - University of Glasgow, 2007.
MSc(R) thesis submitted to the Faculty of Information and Mathematical Sciences, Department of Computing Science, University of Glasgow, 2007. Includes bibliographical references. Print version also
MSc(R) thesis submitted to the Faculty of Information and Mathematical Sciences, Department of Computing Science, University of Glasgow, 2007. Includes bibliographical references. Print version also
Externí odkaz:
http://theses.gla.ac.uk/304/
We explore probability modelling of discretization uncertainty for system states defined implicitly by ordinary or partial differential equations. Accounting for this uncertainty can avoid posterior under-coverage when likelihoods are constructed fro
Externí odkaz:
http://arxiv.org/abs/1306.2365
The paper proposes a Riemannian Manifold Hamiltonian Monte Carlo sampler to resolve the shortcomings of existing Monte Carlo algorithms when sampling from target densities that may be high dimensional and exhibit strong correlations. The method provi
Externí odkaz:
http://arxiv.org/abs/0907.1100