Zobrazeno 1 - 10
of 68
pro vyhledávání: '"Ray, Kolyan"'
We propose a scalable variational Bayes method for statistical inference for a single or low-dimensional subset of the coordinates of a high-dimensional parameter in sparse linear regression. Our approach relies on assigning a mean-field approximatio
Externí odkaz:
http://arxiv.org/abs/2406.12659
We consider nonparametric statistical inference on a periodic interaction potential $W$ from noisy discrete space-time measurements of solutions $\rho=\rho_W$ of the nonlinear McKean-Vlasov equation, describing the probability density of the mean fie
Externí odkaz:
http://arxiv.org/abs/2404.16742
Autor:
Travis, Luke, Ray, Kolyan
We study pointwise estimation and uncertainty quantification for a sparse variational Gaussian process method with eigenvector inducing variables. For a rescaled Brownian motion prior, we derive theoretical guarantees and limitations for the frequent
Externí odkaz:
http://arxiv.org/abs/2310.00097
We introduce Group Spike-and-slab Variational Bayes (GSVB), a scalable method for group sparse regression. A fast co-ordinate ascent variational inference (CAVI) algorithm is developed for several common model families including Gaussian, Binomial an
Externí odkaz:
http://arxiv.org/abs/2309.10378
We establish a general Bernstein--von Mises theorem for approximately linear semiparametric functionals of fractional posterior distributions based on nonparametric priors. This is illustrated in a number of nonparametric settings and for different c
Externí odkaz:
http://arxiv.org/abs/2301.08158
Autor:
Hoffmann, Marc, Ray, Kolyan
We consider nonparametric Bayesian inference in a multidimensional diffusion model with reflecting boundary conditions based on discrete high-frequency observations. We prove a general posterior contraction rate theorem in $L^2$-loss, which is applie
Externí odkaz:
http://arxiv.org/abs/2211.12267
Autor:
Wang, Junyang, Ray, Kolyan, Brito-Parada, Pablo, Plancherel, Yves, Bide, Tom, Mankelow, Joseph, Morley, John, Stegemann, Julia, Myers, Rupert
Material Flow Analysis (MFA) is used to quantify and understand the life cycles of materials from production to end of use, which enables environmental, social and economic impacts and interventions. MFA is challenging as available data is often limi
Externí odkaz:
http://arxiv.org/abs/2211.06178
We rigorously prove that deep Gaussian process priors can outperform Gaussian process priors if the target function has a compositional structure. To this end, we study information-theoretic lower bounds for posterior contraction rates for Gaussian p
Externí odkaz:
http://arxiv.org/abs/2205.07764
Few Bayesian methods for analyzing high-dimensional sparse survival data provide scalable variable selection, effect estimation and uncertainty quantification. Such methods often either sacrifice uncertainty quantification by computing maximum a post
Externí odkaz:
http://arxiv.org/abs/2112.10270
Autor:
Giordano, Matteo, Ray, Kolyan
Publikováno v:
Ann. Statist. 50(5) (2022), 2872-2898
We study nonparametric Bayesian models for reversible multi-dimensional diffusions with periodic drift. For continuous observation paths, reversibility is exploited to prove a general posterior contraction rate theorem for the drift gradient vector f
Externí odkaz:
http://arxiv.org/abs/2012.12083