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Sparse variational approximations allow for principled and scalable inference in Gaussian Process (GP) models. In settings where several GPs are part of the generative model, theses GPs are a posteriori coupled. For many applications such…

Machine Learning · Statistics 2017-11-30 Vincent Adam

For scalar field theory, a new generalization of the Exact RG to curved space is proposed, in which the conformal anomaly is explicitly present. Vacuum terms require regularization beyond that present in the canonical formulation of the…

High Energy Physics - Theory · Physics 2022-03-01 Oliver J. Rosten

We study maximum likelihood estimation for spatial generalized linear mixed models with Gaussian process approximations using a stochastic Newton-Raphson algorithm. We consider two Gaussian Process approximations in this context: spectral…

Methodology · Statistics 2026-05-19 Samuel I. Watson , Yixin Wang , Emanuele Giorgi

We present analytical implementation of conformal field theory on a compact Riemann surface. We consider statistical fields constructed from background charge modifications of the Gaussian free field and derive Ward identities which…

Mathematical Physics · Physics 2018-03-13 Nam-Gyu Kang , Nikolai G. Makarov

The wave function in the quantum theory of the O(N) extended supersymmetric particle model describes a massless free field with spin N/2. This quantum theory is here exactly solved in terms of gauge fields in arbitrary even dimensions using…

High Energy Physics - Theory · Physics 2009-06-12 Robert Marnelius

In a recent paper the author obtained optimal bounds for the strong Gaussian approximation of sums of independent $\R^d$-valued random vectors with finite exponential moments. The results may be considered as generalizations of well-known…

Probability · Mathematics 2007-05-23 A. Yu. Zaitsev

Sparse variational approximations are popular methods for scaling up inference and learning in Gaussian processes to larger datasets. For $N$ training points, exact inference has $O(N^3)$ cost; with $M \ll N$ features, state of the art…

Machine Learning · Statistics 2024-04-15 Talay M Cheema , Carl Edward Rasmussen

By the appropriate use of the Fock-Schwinger gauge properties, we derive the closed integral form of the `point-split' non-local background gauge connection originally expressed as a finite sum. This is achieved in the limit when the finite…

High Energy Physics - Theory · Physics 2007-05-23 Emmanuel T. Rodulfo , Joseph Ambrose G. Pagaran

Vecchia's approximate likelihood for Gaussian process parameters depends on how the observations are ordered, which can be viewed as a deficiency because the exact likelihood is permutation-invariant. This article takes the alternative…

Computation · Statistics 2018-02-20 Joseph Guinness

The standard perturbative effective potential suffers from two related problems of principle involving the field-dependent Goldstone boson squared mass, G. First, in general G can be negative, and it actually is negative in the Standard…

High Energy Physics - Phenomenology · Physics 2014-08-06 Stephen P. Martin

We develop flexible methods of deriving variational inference for models with complex latent variable structure. By splitting the variables in these models into "global" parameters and "local" latent variables, we define a class of…

Computation · Statistics 2019-04-23 Linda S. L. Tan , Aishwarya Bhaskaran , David J. Nott

We consider a free massless scalar field coupled to an infinite tower of background higher-spin gauge fields via minimal coupling to the traceless conserved currents. The set of Abelian gauge transformations is deformed to the non-Abelian…

High Energy Physics - Theory · Physics 2011-02-22 Xavier Bekaert , Euihun Joung , Jihad Mourad

The effective action with homogeneous valence (off-diagonal) gluons as background fields in the extended SU(2) model of QCD is obtained in one-loop approximation. We keep the manifest gauge and Lorentz invariance during whole calculation by…

High Energy Physics - Theory · Physics 2007-05-23 Y. M. Cho , Haewon Lee , D. G. Pak

The possibility of having a gauge fixing term in the effective Lagrangian that is not a quadratic expression has been explored in spin-two theories so as to have a propagator that is both traceless and transverse. We first show how this…

High Energy Physics - Theory · Physics 2014-11-18 F. T. Brandt , D. G. C. McKeon

The Gaussian Effective Potential in a fixed transverse unitarity gauge is studied for the static three-dimensional U(1) scalar electrodynamics (Ginzburg-Landau phenomenological theory of superconductivity). In the broken-symmetry phase the…

Superconductivity · Physics 2007-05-23 M. Camarda , G. G. N. Angilella , R. Pucci , F. Siringo

We consider a self-interacting scalar field theory in a slowly varying gravitational background field. Using zeta-function regularization and heat-kernel techniques, we derive the one-loop effective Lagrangian up to second order in the…

High Energy Physics - Theory · Physics 2009-10-22 Klaus Kirsten , Guido Cognola , Luciano Vanzo

We analyze the Gaussian approximation as a method to obtain the first and second moments of a stochastic process described by a master equation. We justify the use of this approximation with ideas coming from van Kampen's expansion approach…

Statistical Mechanics · Physics 2015-05-18 Luis F. Lafuerza , Raul Toral

We study the decoupling effects in one-loop corrected N=1 supersymmetric theory with gauge neutral chiral superfields, by calculating the one-loop corrected effective Lagrangian that involves light and heavy fields with the mass scale M,…

High Energy Physics - Theory · Physics 2009-09-17 I. L. Buchbinder , M. Cvetic , A. Yu. Petrov

We derive recursively the perturbation series for the ground-state energy of the D-dimensional anharmonic oscillator and resum it using variational perturbation theory (VPT). From the exponentially fast converging approximants, we extract…

Quantum Physics · Physics 2009-12-06 Sebastian F. Brandt , Axel Pelster

Variational methods are attractive for computing Bayesian inference for highly parametrized models and large datasets where exact inference is impractical. They approximate a target distribution - either the posterior or an augmented…

Computation · Statistics 2019-11-21 Michael Stanley Smith , Ruben Loaiza-Maya , David J. Nott
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