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Готові списки джерел за темами / Reparameterisation / Статті в журналах

Статті в журналах з теми "Reparameterisation"

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Автор: Grafiati

Опубліковано: 10 грудня 2022

Оновлено: 28 січня 2023

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1

Tan, Willie. "LEAST SQUARES ADJUSTMENT THROUGH REPARAMETERISATION." Survey Review 36, no. 283 (January 2002): 362–66. http://dx.doi.org/10.1179/sre.2002.36.283.362.

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2

Shao, Quanxi. "A REPARAMETERISATION METHOD FOR EMBEDDED MODELS." Communications in Statistics - Theory and Methods 31, no. 5 (May 23, 2002): 683–97. http://dx.doi.org/10.1081/sta-120003647.

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3

Steel, S. J., and N. J. le Roux. "A Reparameterisation Of A Bivariate Gamma Extension." Communications in Statistics - Theory and Methods 16, no. 1 (January 1987): 293–305. http://dx.doi.org/10.1080/03610928708829366.

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4

Hoidn, Hans-Peter. "A reparameterisation method to determine conformal maps." Journal of Computational and Applied Mathematics 14, no. 1-2 (February 1986): 155–61. http://dx.doi.org/10.1016/0377-0427(86)90136-6.

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5

Gunn, Roger N., Michael J. Chappell, and Vincent J. Cunningham. "Reparameterisation of Unidentifiable Systems using the Taylor Series Approach." IFAC Proceedings Volumes 30, no. 2 (March 1997): 247–52. http://dx.doi.org/10.1016/s1474-6670(17)44579-4.

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6

Sharkey, Paul, and Jonathan A. Tawn. "A Poisson process reparameterisation for Bayesian inference for extremes." Extremes 20, no. 2 (December 17, 2016): 239–63. http://dx.doi.org/10.1007/s10687-016-0280-2.

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7

Luke, Michael, and Aneesh V. Manohar. "Reparameterisation invariance constraints on heavy particle effective field theories." Physics Letters B 286, no. 3-4 (July 1992): 348–54. http://dx.doi.org/10.1016/0370-2693(92)91786-9.

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8

Ramroach, Sterling, and Ajay Joshi. "Accelerating Data-Parallel Neural Network Training with Weighted-Averaging Reparameterisation." Parallel Processing Letters 31, no. 02 (May 6, 2021): 2150009. http://dx.doi.org/10.1142/s0129626421500092.

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Анотація:

Recent advances in artificial intelligence has shown a direct correlation between the performance of a network and the number of hidden layers within the network. The Compute Unified Device Architecture (CUDA) framework facilitates the movement of heavy computation from the CPU to the graphics processing unit (GPU) and is used to accelerate the training of neural networks. In this paper, we consider the problem of data-parallel neural network training. We compare the performance of training the same neural network on the GPU with and without data parallelism. When data parallelism is used, we compare with both the conventional averaging of coefficients and our proposed method. We set out to show that not all sub-networks are equal and thus, should not be treated as equals when normalising weight vectors. The proposed method achieved state of the art accuracy faster than conventional training along with better classification performance in some cases.

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9

Korsgaard, Inge, Anders Andersen, and Daniel Sorensen. "A useful reparameterisation to obtain samples from conditional inverse Wishart distributions." Genetics Selection Evolution 31, no. 2 (1999): 177. http://dx.doi.org/10.1186/1297-9686-31-2-177.

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10

Robert, Christian P., and Kerrie L. Mengersen. "Reparameterisation Issues in Mixture Modelling and their bearing on MCMC algorithms." Computational Statistics & Data Analysis 29, no. 3 (January 1999): 325–43. http://dx.doi.org/10.1016/s0167-9473(98)00058-9.

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11

Cotter, Colin J., Allan Clark, and Joaquim Peiró. "A Reparameterisation Based Approach to Geodesic Constrained Solvers for Curve Matching." International Journal of Computer Vision 99, no. 1 (March 3, 2012): 103–21. http://dx.doi.org/10.1007/s11263-012-0520-0.

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12

Bishop, S. C., and K. Mackenzie. "The inheritance of traits describing early lamb performance in Scottish Blackface sheep." Proceedings of the British Society of Animal Science 2001 (2001): 43. http://dx.doi.org/10.1017/s1752756200004257.

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Анотація:

Ewe prolificacy, lamb survival and early lamb growth are economically important traits, playing a major role in determining the profitability of hill sheep farms (Coningtonet al., 2000). In currently advocated selection strategies these traits are usually expressed as traits of the dam, however, relatively low heritabilities limit the genetic progress achievable (Coningtonet al., 2001). An alternative approach is to treat these traits, where appropriate, as traits of both the ewe and lamb. This study investigates reparameterisation of these traits as joint expressions of both lamb and ewe genotype, and considers the impact of the results on currently-advocated selection strategies.

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13

Cheung, S. Y. Amy, Oneeb Majid, James W. T. Yates, and Leon Aarons. "Structural identifiability analysis and reparameterisation (parameter reduction) of a cardiovascular feedback model." European Journal of Pharmaceutical Sciences 46, no. 4 (July 2012): 259–71. http://dx.doi.org/10.1016/j.ejps.2011.12.017.

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14

Cox, S. M., and A. J. Roberts. "Centre manifolds of forced dynamical systems." Journal of the Australian Mathematical Society. Series B. Applied Mathematics 32, no. 4 (April 1991): 401–36. http://dx.doi.org/10.1017/s0334270000008511.

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Анотація:

AbstractCentre manifolds arise in a rational approach to the problem of forming low-dimensional models of dynamical systems with many degrees of freedom. When a dynamical system with a centre manifold is subject to a small forcing, F, there are two effects: to displace the centre manifold; and to alter the evolution thereon. We propose a formal scheme for calculating the centre manifold of such a forced dynamical system. Our formalism permits the calculation of these effects, with errors of order |F|2. We find that the displacement of the manifold allows a reparameterisation of its description, and we describe two “natural” ways in which this can be carried out. We give three examples: an introductory example; a five-mode model of the atmosphere to display the quasi-geostrophic approximation; and the forced Kuramoto-Sivashinsky equation.

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15

Shevchenko, Mikhail, David Windridge, and Josef Kittler. "A linear-complexity reparameterisation strategy for the hierarchical bootstrapping of capabilities within perception–action architectures." Image and Vision Computing 27, no. 11 (October 2009): 1702–14. http://dx.doi.org/10.1016/j.imavis.2008.12.002.

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16

Żogała-Siudem, Barbara, Anna Cena, Grzegorz Siudem, and Marek Gagolewski. "Interpretable reparameterisations of citation models." Journal of Informetrics 17, no. 1 (February 2023): 101355. http://dx.doi.org/10.1016/j.joi.2022.101355.

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17

Evans, N. D., M. J. Chappell, K. R. Godfrey, and M. J. Chapman. "An Algorithm for Generating Locally Identifiable Reparameterisations of Unidentifiable Systems." IFAC Proceedings Volumes 33, no. 3 (March 2000): 53–58. http://dx.doi.org/10.1016/s1474-6670(17)35488-5.

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18

Evans, Neil D., and Michael J. Chappell. "Extensions to a procedure for generating locally identifiable reparameterisations of unidentifiable systems." Mathematical Biosciences 168, no. 2 (December 2000): 137–59. http://dx.doi.org/10.1016/s0025-5564(00)00047-x.

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19

Chappell, Michael J., and Roger N. Gunn. "A procedure for generating locally identifiable reparameterisations of unidentifiable non-linear systems by the similarity transformation approach." Mathematical Biosciences 148, no. 1 (February 1998): 21–41. http://dx.doi.org/10.1016/s0025-5564(97)10004-9.

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20

Meyer, Karin. "Reducing computational demands of restricted maximum likelihood estimation with genomic relationship matrices." Genetics Selection Evolution 55, no. 1 (January 25, 2023). http://dx.doi.org/10.1186/s12711-023-00781-7.

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AbstractRestricted maximum likelihood estimation of genetic parameters accounting for genomic relationships has been reported to impose computational burdens which typically are many times higher than those of corresponding analyses considering pedigree based relationships only. This can be attributed to the dense nature of genomic relationship matrices and their inverses. We outline a reparameterisation of the multivariate linear mixed model to principal components and its effects on the sparsity pattern of the pertaining coefficient matrix in the mixed model equations. Using two data sets we demonstrate that this can dramatically reduce the computing time per iterate of the widely used ‘average information’ algorithm for restricted maximum likelihood. This is primarily due to the fact that on the principal component scale, the first derivatives of the coefficient matrix with respect to the parameters modelling genetic covariances between traits are independent of the relationship matrix between individuals, i.e. are not afflicted by a multitude of genomic relationships.

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21

Avanzi, Benjamin, Greg Taylor, and Melantha Wang. "SPLICE: a synthetic paid loss and incurred cost experience simulator." Annals of Actuarial Science, May 23, 2022, 1–29. http://dx.doi.org/10.1017/s1748499522000057.

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Abstract In this paper, we first introduce a simulator of cases estimates of incurred losses called SPLICE (Synthetic Paid Loss and Incurred Cost Experience). In three modules, case estimates are simulated in continuous time, and a record is output for each individual claim. Revisions for the case estimates are also simulated as a sequence over the lifetime of the claim in a number of different situations. Furthermore, some dependencies in relation to case estimates of incurred losses are incorporated, particularly recognising certain properties of case estimates that are found in practice. For example, the magnitude of revisions depends on ultimate claim size, as does the distribution of the revisions over time. Some of these revisions occur in response to occurrence of claim payments, and so SPLICE requires input of simulated per-claim payment histories. The claim data can be summarised by accident and payment “periods” whose duration is an arbitrary choice (e.g. month, quarter, etc.) available to the user. SPLICE is built on an existing simulator of individual claim experience called SynthETIC (introduced in Avanzi et al. 2021b, Insurance: Mathematics and Economics, 100, 296–308), which offers flexible modelling of occurrence, notification, as well as the timing and magnitude of individual partial payments. This is in contrast with the incurred losses, which constitute the additional contribution of SPLICE. The inclusion of incurred loss estimates provides a facility that almost no other simulators do. SPLICE is is a fully documented R package that is publicly available and open source (on CRAN). SPLICE, combined with SynthETIC, provides 11 modules (occurrence, notification, etc.), any one or more of which may be re-designed according to the user’s requirements. It comes with a default version that is loosely calibrated to resemble a specific (but anonymous) Auto Bodily Injury portfolio, as well as data generation functionality that outputs alternative data sets under a range of hypothetical scenarios differing in complexity. The general structure is suitable for most lines of business, with some reparameterisation.

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22

Baldazzi, Alessio, Riccardo Ben Ali Zinati, and Kevin Falls. "Essential renormalisation group." SciPost Physics 13, no. 4 (October 7, 2022). http://dx.doi.org/10.21468/scipostphys.13.4.085.

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Анотація:

We propose a novel scheme for the exact renormalisation group motivated by the desire of reducing the complexity of practical computations. The key idea is to specify renormalisation conditions for all inessential couplings, leaving us with the task of computing only the flow of the essential ones. To achieve this aim, we utilise a renormalisation group equation for the effective average action which incorporates general non-linear field reparameterisations. A prominent feature of the scheme is that, apart from the renormalisation of the mass, the propagator evaluated at any constant value of the field maintains its unrenormalised form. Conceptually, the simplifications can be understood as providing a description based only on quantities that enter expressions for physical observables since the redundant, non-physical content is automatically disregarded. To exemplify the scheme’s utility, we investigate the Wilson-Fisher fixed point in three dimensions at order two in the derivative expansion. In this case, the scheme removes all order \partial^2∂2 operators apart from the canonical term. Further simplifications occur at higher orders in the derivative expansion. Although we concentrate on a minimal scheme that reduces the complexity of computations, we propose more general schemes where inessential couplings can be tuned to optimise a given approximation. We further discuss the applicability of the scheme to a broad range of physical theories.

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