Zobrazeno 1 - 10
of 230
pro vyhledávání: '"OATES, CHRIS J."'
This book aims to provide a graduate-level introduction to advanced topics in Markov chain Monte Carlo (MCMC) algorithms, as applied broadly in the Bayesian computational context. Most, if not all of these topics (stochastic gradient MCMC, non-revers
Externí odkaz:
http://arxiv.org/abs/2407.12751
Autor:
Shen, Zheyang, Oates, Chris J.
Diffusion models are typically trained using score matching, yet score matching is agnostic to the particular forward process that defines the model. This paper argues that Markov diffusion models enjoy an advantage over other types of diffusion mode
Externí odkaz:
http://arxiv.org/abs/2406.09084
An informal observation, made by several authors, is that the adaptive design of a Markov transition kernel has the flavour of a reinforcement learning task. Yet, to-date it has remained unclear how to actually exploit modern reinforcement learning t
Externí odkaz:
http://arxiv.org/abs/2405.13574
We develop and describe online algorithms for performing real-time semiparametric regression analyses. Earlier work on this topic is in Luts, Broderick & Wand (J. Comput. Graph. Statist., 2014) where online mean field variational Bayes was employed.
Externí odkaz:
http://arxiv.org/abs/2310.12391
Stein discrepancies have emerged as a powerful tool for retrospective improvement of Markov chain Monte Carlo output. However, the question of how to design Markov chains that are well-suited to such post-processing has yet to be addressed. This pape
Externí odkaz:
http://arxiv.org/abs/2305.10068
Control variates can be a powerful tool to reduce the variance of Monte Carlo estimators, but constructing effective control variates can be challenging when the number of samples is small. In this paper, we show that when a large number of related i
Externí odkaz:
http://arxiv.org/abs/2303.04756
The Mat\'ern model has been a cornerstone of spatial statistics for more than half a century. More recently, the Mat\'ern model has been central to disciplines as diverse as numerical analysis, approximation theory, computational statistics, machine
Externí odkaz:
http://arxiv.org/abs/2303.02759
This work provides theoretical foundations for kernel methods in the hyperspherical context. Specifically, we characterise the native spaces (reproducing kernel Hilbert spaces) and the Sobolev spaces associated with kernels defined over hyperspheres.
Externí odkaz:
http://arxiv.org/abs/2211.09196
Autor:
Oates, Chris. J.
For two decades, reproducing kernels and their associated discrepancies have facilitated elegant theoretical analyses in the setting of quasi Monte Carlo. These same tools are now receiving interest in statistics and related fields, as criteria that
Externí odkaz:
http://arxiv.org/abs/2210.16357
We analyse the calibration of BayesCG under the Krylov prior, a probabilistic numeric extension of the Conjugate Gradient (CG) method for solving systems of linear equations with symmetric positive definite coefficient matrix. Calibration refers to t
Externí odkaz:
http://arxiv.org/abs/2208.03885